- There is no expectation that the your progress within the categories move in lockstep; in fact, it is highly unlikely that you fall into just one level for every category.
- Rather, your level is determined by whether the majority (currently, 3+) of categories fall into a particular level.
- The bullets outlined in each category are the minimum requirements for you to be at this level in that category.
- Right before the Data Scientist V level*, the track splits into parallel tracks, one for management and one for individual contributor.
- Here, expectations ramp up dramatically: each successive level now can be considered as representing a different job role than the previous level.
- However, on either of the tracks, the expectations indicate a level of broad leadership, that requires thought leadership, as well as the ability to engage groups of people both within technology and outside of it to make bigger and bigger things happen.
- The question to ask yourself here is “Do I want to build bigger and better systems, or do I want to manage bigger and better teams?”
- These ladders were in large part built off of the awesome engineering ladders and best practices leveraged by e.g. Rent the Runway, Foursquare, Google, Airbnb, StitchFix, and Amazon. The majority of these ladders seemed to converge on approximately 7-8 levels, which is similarly reflected in these.
- To date, there are no publicly available data science career ladders, to our knowledge. As such, these ladders were built, iterated on & discussed dozens of times, and refined with representatives from human resources, data science team leads and people managers.
- It is totally anticipated that these ladders will evolve over time! If individuals on the team believe more levels should be added - or should be trimmed - to reflect the current state of the team and the industry, this is something your team should expect to discuss and incorporate accordingly.
- Senior executives are likely either above or outside of the leveling (hence the "lightness" in level VII).
- Importantly, you will notice that as you progress, the number of critical skills required for you to perform at the highest levels tend to fall in the categories centered around delivery, impact, and communication and leadership, regardless of which track you pursue.
- Of course, these skills are necessarily built on core foundational technological skills, and it is an expectation (as outlined in the ladders) that you establish methods / processes / ways of working for yourself to stay abreast of industry-wide technological changes.
- However, your core competencies as a team member and leader on this team as you grow in your career will rely heavily on collaborative skills and empathy, aligned with the values of your team.
- It is highly recommended that before being promoted from Data Scientist IV to Data Scientist V, an individual has the experience of having the responsibility and accountability associated with being a squad lead. This will allow you to more effectively make an informed decision about which track you are currently interested in pursuing.
Data Scientist I | Data Scientist II | Data Scientist III | Data Scientist IV | Data Scientist V | Data Scientist VI | Data Scientist VII
Impact and influence
- capable of prioritizing and completing tasks with clear direction on process and outcomes
- effectively leverages team practices, procedures and tools (e.g. agile, data science cookie cutter repo, filepath naming conventions, etc) to complete work, and to minimize repeat mistakes
- has begun to develop and apply the core skills and knowledge required for the role (e.g. learning source control / editors, the build system, unittest, self-documenting code, etc)
Leadership and teamwork
- demonstrates company's core values and principles in approach to work, and partnership with stakeholders, clients, and teammates
- demonstrates flexibility and goes beyond immediate role to assist others with day-to-day responsibilities
Communication
- is effective in communicating status to the team
Software development and engineering skills
- focused on learning and using the team's and firm's existing tools, resources, and processes, drawing upon previous experience in software development
Statistical inference and mathematical modeling
- broad knowledge of basic statistical concepts (Bachelors’ equivalent)
Impact and influence
- capable of prioritizing and completing tasks with clear direction on process and outcomes
- effectively leverages team practices, procedures and tools (e.g. agile, data science cookie cutter repo, filepath naming conventions, etc) to complete work, and to minimize repeat mistakes
- has begun to develop and apply the core skills and knowledge required for the role (e.g. learning source control / editors, the build system, unittest, self-documenting code, etc)
- has begun to own small-to-medium features for technical design, through to completion
- is able to know when to ask for help, in order to get themselves unblocked
Leadership and teamwork
- demonstrates company's core values and principles in approach to work, and partnership with stakeholders, clients, and teammates
- demonstrates flexibility and goes beyond immediate role to assist others with day-to-day responsibilities
Communication
- is effective in communicating status to the team
- clearly communicates assumptions, gets clarification on tasks up front to minimize the need for rework, and vocalizes problems they have identified with requirements for their own work
- understands and can communicate to individuals outside their squad how their work fits into the larger project
- demonstrates an eagerness to find ways to improve by soliciting feedback from others, and simultaneously gives timely and helpful feedback to peers and managers
- actively demonstrates empathy in situations where there are inevitable goal conflicts through collaboration, compromise and clear communication
Software development and engineering skills
- focused on learning and using the team's / company's existing tools, resources, and processes, drawing upon previous experience in software development
- writes correct & clean code with guidance, and strives to consistently follow industry and team-wide stated best practices
- demonstrates the ability to learn quickly, and strives to make steady progress without the need for constant significant feedback from more senior engineers
- participant in technical design of features, with guidance
Statistical inference and mathematical modeling
- broad knowledge of basic statistical concepts (Bachelors’ equivalent)
- primary focus at this stage of their career is growing as a statistician, for example by learning the company's tools / resources / processes, as well as the relationship between data scientists and data engineers within the data science organization and at the company more broadly
- strives to understand the underlying assumptions and mechanics of machine learning models and existing libraries, the theory around the development of said models
- fully understands that data cleaning is the majority of data science work, strives to consistently followed stated best practices (in industry, at the company, and in their respective squad and within the team)
- actively participates in statistical design with guidance; this includes feature generation, model selection, assumption testing
- regularly engaging in conversations with manager about benefits / trade-offs of choosing to develop deep expertise in a particular modeling area, or to develop breadth of experience across a range of modeling areas
Impact and influence
- demonstrates persistence in the face of roadblocks, dispatching them efficiently and pulling in others to help as necessary, sooner rather than later
- demonstrates the initiative to fix issues before being explicitly assigned to them (this assumes such a culture is fostered on the squad)
- delivers increasingly complex product features that (they believe) are well-baked and bug-free
Leadership and teamwork
- seeks and uses empirical evidence through proof of concepts / tests / external research when identifying and articulating problems / risks of their own work or others'
- contributes consistently + more than once ("regularly") to Lunch & Learns
- provides mentorship to junior engineers and teammates via pairing, design and code reviews
- demonstrates an understanding of the business impact and business units impacted by the work completed on their squad, and uses that understanding to drive requirements that take into account all parties' needs
- consistently exhibits empathy for the user of the software being built, and uses that as a guide for decision-making
Communication
- regularly and effectively communicates technical decisions through design docs, tech talks, and internal team wiki
- practices communicating across functional groups to become better at effective cross-group communication, ultimately enabling the ability to work well with individuals and teams across different groups and with different incentive structures (e.g. product management, stakeholders, etc)
- proactively identifies and vocalizes challenges with requirements for both their own work and adjacent work (for example, lack of clarity, inconsistencies, technical limitations)
Software development and engineering skills
- contributes to and is able to work in other areas of their squad's or the larger team's codebase, with guidance
Statistical inference and mathematical modeling
- understands and makes well-reasoned design decisions and tradeoffs in their area of data science work
- demonstrates the ability to dive into current data sources with minimal to no guidance, and ideates on sources for additional where gaps are apparent
- comfortable with inherent initial ambiguity of iterative data science cycle, and independently takes on modeling and analysis tasks
- demonstrates the ability to identify, then learn, new data processing technologies where needed
- consistently able to independently troubleshoot and resolves / debugs issues
- demonstrates knowledge of industry trends, and the team and company's infrastructure and build system (including bitbucket / git / 4D)
Impact and influence
- demonstrates persistence in the face of roadblocks, dispatching them efficiently and pulling in others to help as necessary, sooner rather than later
- demonstrates the initiative to fix issues before being explicitly assigned to them (this assumes such a culture is fostered on the squad)
- delivers increasingly complex product features that (they believe) are well-baked and bug-free
- has end-to-end responsibility on product building-blocks, or entire products, that are increasingly complex over time; contributes to common code (again, this assumes a sufficent culture on the squad to support these)
- identifies & proactively tackles technical debt before it grows to be "too large" i.e. into debt that requires significant upfront work to resolve
- scopes & stages work into well-defined milestones to avoid monolithic deliverable; is constantly working to make accurate timeline estimates and deliver to those estimates
- owns technical testing, measuring performance for their products, and is developing a reputation for having drama-free launches
Leadership and teamwork
- seeks and uses empirical evidence through proof of concepts / tests / external research when identifying and articulating problems / risks of their own work or others'
- contributes consistently + more than once ("regularly") to Lunch & Learns
- provides mentorship to junior engineers and teammates via pairing, design and code reviews
- demonstrates an understanding of the business impact and business units impacted by the work completed on their squad, and uses that understanding to drive requirements that take into account all parties' needs
- consistently exhibits empathy for the user of the software being built, and uses that as a guide for decision-making
- takes initiative to identify and solve high-impact ("important") problems, coordinating with others on cross-cutting technical issues
- helps set the direction at the project / service level, and consistently influences decision-making at the squad level
- contributes to the culture of learning at the team level by providing help when asked
Communication
- regularly and effectively communicates technical decisions through design docs, tech talks, and internal team wiki
- practices communicating across functional groups to become better at effective cross-group communication, ultimately enabling the ability to work well with individuals and teams across different groups and with different incentive structures (e.g. product management, stakeholders, etc)
- proactively identifies and vocalizes challenges with requirements for both their own work and adjacent work (for example, lack of clarity, inconsistencies, technical limitations)
- strives to help teammates grow and improve as individual contributors through code reviews, documentation, technical guidance, mentoring (OR serving as a Tech Lead on a project, see *)
- communicates industry-wide and sets team-wide best standards
- strives to sit on the model or architecture review group
- understands the tradeoffs between technical, analytical, & product needs and communicates the "best" solution that manages those tradeoffs and takes all of these needs into account
- is learning how to get buy-in on solutions to technical problems affecting their team
- proactively identifies and proposes strategies around technical problems affecting their team
- continues to actively solicit feedback, and provides helpful feedback to squad- and teammates on projects outside their core area
Software development and engineering skills
- contributes to and is able to work in other areas of their squad's or the larger team's codebase, with guidance
Statistical inference and mathematical modeling
- understands and makes well-reasoned design decisions and tradeoffs in their area of data science work
- demonstrates the ability to dive into current data sources with minimal to no guidance, and ideates on sources for additional where gaps are apparent
- comfortable with inherent initial ambiguity of iterative data science cycle, and independently takes on modeling and analysis tasks
- demonstrates the ability to identify, then learn, new data processing technologies where needed
- consistently able to independently troubleshoot and resolves / debugs issues
- demonstrates knowledge of industry trends, and the team and company's infrastructure and build system (including bitbucket / git / 4D)
- sought out for guidance in a specific area of statistics or machine learning, or on a building block / component of the product they are building
- anticipates potential challenges and technical issues with implementing different modeling techniques, and makes data and model implementation design decisions to avoid them
- owner of & expert on large sections of our codebase
- responsible for eliciting data science problem formulation from partner teams, and achieve agreement & alignment from them
- can be trusted to map product requirements in order to determine the algorithm / model output that needs to be built
At this point, the career paths split into individual contributor (IC) and manager.
| IC | Manager | |
|---|---|---|
| Impact and influence | consistently able to reduce the complexity of projects, services & processes in order to get more done with less work; developing a reputation as a prolific contributor to core & side projects | excels at focusing the team on highest impact projects; proactively identifies and clears roadblocks for the team |
| owns and ships multiple large products or services, complex libraries or major pieces of infrastructure / software | partners effectively with relevant product stakeholders and users to manage the scope of deliverables for the technical component of the product roadmap; shields team from unnecessary "noise" | |
| contributes to code reviews, bug fixes, and building small features without becoming a bottleneck for the team | ||
| contributes to leading recruiting efforts, and determines and vocalizes (early on) headcount-needs for their team | ||
| Leadership and teamwork | has made an obvious positive impact on the entire company’s technical trajectory | is focused on developing and iterating upon their leadership style, while simultaneously managing team members independently; builds a culture of trust so that reports are empowered to take both responsibility and accountability for their work |
| demonstrates an ability to think strategically through product development plan and how it fits into the larger team product ecosystem, and uses that thinking to shape broad architecture decisions both for the product itself but also to provide a perspective on other team products | recognizes the crucialness of diversity of perspective on their team, and is developing the ability to manage team members with different skill sets, technical areas of focus, and ways of working AND being motivated; is actively working towards becoming comfortable with inevitable "failures" and mistakes, and lets them go as opposed to holding it against team members over long periods of time | |
| contributes to a strong culture of mentorship and inclusion for all members by acting as a mentor (formally or informally) and advocating for individuals in meetings they may not be in | leads the delivery of major initiatives on clear timelines; surfaces requirements to upper management early on | |
| consistently able to identify areas of strategic technical debt, and provides cost / benefit analysis for eliminating this debt, by, for example, developing timelines for how to prioritize resolutions | ||
| contributes to a strong culture of mentorship and inclusion for all members by acting as a mentor (formally or informally) and advocating for individuals in meetings they not be in | ||
| Communication | demonstrates the ability to actively listen, and guides debates to help reach a consensus; once a decision is made, clearly communicates and backs that decision | demonstrates the ability to actively listen, and guides debates to help reach a consensus; once a decision is made, clearly communicates and backs that decision |
| leads the creation of a culture of trust so individual contributors on squad and team feel empowered to own and be responsible and accountable for the work to be done | communicates organizational and business context to team members | |
| multiplies the effectiveness of others by facilitating cross-team work | is a conduit for communicating information down and up, provides clarity especially when communicating expectations, and advocates for team members in meetings they are not in | |
| contributes to setting the team's short- to medium-term strategic technical direction | refining their role as a "connector" - to people, resources etc - to help individual team members develop both their hard and "harder" skills | |
| demonstrates the ability to look ahead 6-12 months and identify and put in place solutions to address areas of greatest need in the future | strives to understand the purpose of this job for the individual members on their team, and uses that to inform their management style | |
| simultaneously initiates and executes on plans to help improve individuals' technical skills on squad, if operating as a squad lead; fully leverages individual contributors on squad, taking into account their strengths and where they would like to improve | consistently and actively seeks out feedback from junior team members, peers and leadership, and acts upon said feedback | |
| consistently and actively seeks out feedback from junior team members, peers and leadership, and acts upon said feedback | actively developing the skill of having difficult conversations and providing concrete, specific and useful feedback, in the method appropriate for the situation at hand (this includes timing and channel of communication, specific to individual team members, and is not just limited to formal company mid-year and end-of-year evaluations) | |
| strives to understand the career and personal goals of individuals on their team, and helps provide specific guidance in developing their team member's practical goals and OKRs | ||
| Software development and engineering | provides technical guidance, especially in area of expertise; often sought out for guidance | provides guidance for team members interested in using new technologies or solving proactively-identified technical problems, where necessary |
| anticipates technical issues at the product level, and makes architectural and design decisions to avoid them | ||
| Statistical inference and mathematical modeling | effectively manages the relationship / organizational structure between data engineers and data scientists, ensuring that both parties feel equally empowered, and anticipates technical issues at the product level when interfacing with data scientists | understands the importance of, and consistently sets, appropriate key performance indicators and measurable goals where relevant |
| provides statistical guidance, especially in area of expertise; often sought out for guidance | developing the skill to vocalize and map product requirements across different squads to the roadmap of the team at large | |
| anticipates issues related to modeling at the product level, including ethical implications, influence on product-users' actions, biases in the data, and assumptions in modeling implementation, and makes data and modeling design decisions to avoid them | provides technical guidance, especially in area of expertise; often sought out for guidance | |
| owner & expert in large sections of the data science domain related to their product, for example: the definitions / concepts / derivations of features from different data sources, the exploratory analyses conducted, rationale behind the models being tested, the usefulness and business impact of the data being generated | has developed a solid foundational understanding of agile software development and management, and practices it actively (this includes for example, clear ticket-writing); provides guidance to team members where necessary | |
| sits on model-review board / committee for team, pushes forward approval and documentation per company-wide standards | consistently provides concrete and timely feedback for relevant technical documentation at a team-wide level (at a minimum) | |
| responsible for eliciting data science problem formulation from partner teams, and achieve agreement & alignment from them | ||
| consistently maps product requirements to technical solution, and uses this knowledge to refine and determine the final algorithm(s) to be implemented and delivered |
| IC | Manager | |
|---|---|---|
| Impact and influence | able to consistently deliver large systems involving one or more teams’ contribution on time, at a high level of quality | develops and deploys new strategies for building a high velocity, high performance development organization that is in line with team's emerging customer needs |
| has developed the skill of quickly breaking down complex problems into potential solutions, knowns and unknowns, in order to get to comprehensive and efficient resolutions ("solid resolutions") | strives to consistently build and support an environment conducive for technlogical innovation | |
| continues to develop the ability to debug the most technically complex ("hairiest") problems that the team encounters | leads the creation / continual refinement / active enforcement of the data science organization’s / team's development standards to ensure that data science organization / team's technology can be leveraged as a sustainable competitive advantage | |
| is focused on leading, or developing a reputation as a respected and active participant in, recruiting top talent, conducting performance reviews, and training and career development | ||
| responsible for all headcount planning and personnel evolution for multiple areas of the organization; as necessary, manages vendor and external relations for the organization; consistently quantifies costs and value-add of these decisions and how the impact the budgeting process for the data science organization / team | ||
| provides leadership by proactively nurturing the talent of team members and giving guidance in areas of improvement | ||
| s developing consistency in decision-making around balancing business goals versus technical debt, and uses this knowledge to address technical, resource and personnel issues | ||
| provides a clear understanding of the purpose of the work being done, and consistently prioritizes (and communicates the prioritization of) work to align with the values of the data science organization / team and company | ||
| consistently vocalizes and advocates for solutions that fill strategic gaps | ||
| builds and supports a culture that supports high-functioning and motivated teams | ||
| Leadership and teamwork | plays a key role in developing and iterating on a multi-year technology strategy that encompasses multiple systems and teams for entire critical areas of the data science organization | leverages past experiences, and uses strong communication skills, to collaborate effectively and with empathy with customers, senior management and other business leaders |
| has a reputation for making evidence-driven, nuanced decisions since these decisions, and therefore work, have a direct impact on the long-term success or failure of the team | inspires potential recruits to join the data science organization and company | |
| creates and leads the creation of flexible architecture that enables many potential futures without knowing exactly what the future is; lives by this pattern of behavior and inspires others on the team to follow this pattern of behavior as well | is accountable for owning the OKR-setting and review process for teams under their oversight | |
| builds and reinforces a culture of inclusion by encouraging individuals to speak up in group meetings, lending "privilege," and listening between the lines when junior members of the team speak up | builds and reinforces a culture of inclusion by encouraging individuals to speak up in group meetings, lending "privilege," and listening between the lines when junior members of the team speak up | |
| Communication | consistently identifies areas that the data science organization can share externally (e.g. great work), and communicates or guides the creation of content around those areas | can be relied upon to communicate technical concepts to business stakeholders, and regularly communicates business objectives to technical teams under their oversight |
| primarily acts as a "multiplier" by introducing policies / patterns, building systems or authoring tools of communication, that raise the level of productivity of all members within the data science organization / team | works with legal team, in terms of setting policy & participation in setting standards, and responding to incidents (proactive & reactive) | |
| consistently leads conversations internally about the direction of major areas of the technology at the data science team-level and company-level; builds team-wide consenus to the adoption of this direction, and uses this direction to inspire engineers | ||
| seen as a role model and mentor to every technical member of the team | ||
| Software development and engineering | ensures their organization has appropriately high technical competence, striving for excellence | |
| regularly researches new technologies to stay abreast of industry trends and standards, and uses this to guide what are appropriate solutions to be implemented for the data science organization / team | ||
| Statistical inference and mathematical modeling | anticipates broad technical change(s), ensuring that the data science organization / team is consistently "ahead of the curve," and is able to articulate clearly the scaling & reliability limits of that area | ensures their organization has appropriately high statistical inference and mathematical modeling competence, striving for excellence |
| deeply understands the set of data products for a major part of the company's business, and can identify areas of opportunity and growth for additional data products based on this understanding | regularly researches new technologies to stay abreast of industry trends and standards, and uses this to guide what are appropriate solutions to be implemented for the data science organization / team | |
| has the ability to jump in and help debug and triage critical systems, as needed | ||
| focused on contributing to the architecture by asking the right questions, in order to ensure the architecture and business needs match for that area |
*Purposely light-weight given that senior executive roles are largely outside the scope of these ladders.
| IC | Manager | |
|---|---|---|
| Impact and influence | develops, implements, advocates for and generates buy-in for the team's strategic vision around data products to be built at the company | capable of identifying business growth opportunities enabled by tech, and executes against those opportunities |
| in partnership with the senior-most management (CTO-equivalent), product leads, and other business stakeholders, translates the high-level strategic vision into a clear and actionable technology roadmap | ||
| Leadership and teamwork | strives to ensure that every member of the team understands the business goals for the quarter | Chief Data Officer-equivalent |
| clearly and regularly articulates the personnel and cultural needs in a timely manner that will enable the team to move to the next level, to team members, stakeholders and company C-suite | ||
| ruthlessly prioritizes their time to ensure that every team member is aligned, incentivized, motivated and empowered to execute on said strategy; strives to ensure that every team member understands the business goals for the quarter, and identifies areas for process evolution or clarification | ||
| leads the team in proactively identifying further strategic areas for development | ||
| builds and reinforces a culture of inclusion by encouraging individuals to speak up in group meetings, lending "privilege," and listening between the lines when junior members of the team speak up | ||
| Communication | communicates multi-year technology strategy to team and upwards; strives to communicate to team members clearly enough that every member of the team at all times knows what to prioritize when making day-to-day decisions | regularly communicates executive-level strategy, and strives to ensure that that permeates to every layer at company |
| leads the team in proactively identifying further strategic areas for development | helps break down business directives into technology goals | |
| clearly and regularly articulates the personnel and cultural needs in a timely manner that will enable the team to move to the next level, to team members, stakeholders and company C-suite | ||
| Software development and engineering | sets technical direction | |
| Statistical inference and mathematical modeling | sets technical direction | sets company-wide technical direction, and uses this to set data science team's organizational priorities |
| contributes to growth decisions with a focus on the product and business needs, now and in the future | ensures the data products being built can support multiple future possibilities of the business | |
| greatest technical strength is debugging organizations and processes; knows the right questions to ask to help the team get to the right decision |