A Complete Guide to Data Scientist Career Path

In this guide, we’ll talk about where the data scientist career path could lead and what are the industries ideal for building this path.

Don’t think of this guide as a rigid rule book proscribing how your data scientist career path should look. How your career path will look like depends on you: your interests, your educational background, your skills as a data scientist, the industry you’re in, the (size of the) company you work in, and, of course, a little bit of luck in achieving whatever you want in your professional life.

We want to give you a general overview of what the data scientist career path often looks like. It can unveil some possibilities you didn’t know existed and provide you with direction and new insights into the possibilities as a data scientist.

You can build your data career only where they want you. That’s why it’s a good idea to talk about the industries that give you the most possibilities as a data scientist.

Top Industries for a Data Scientist (And What You’ll Do There)

Data scientists can work virtually in any industry. The only thing they need is data and someone who needs a data scientist.

However, there are a couple of industries that are traditionally top industries for data scientists in terms of demand, advanced tools used, the complexity of business problems, and offered salary.

The salaries are for the US and are sourced from Glassdoor.

Information Technology

Information technology needs data scientists in a wide range of roles. It can be to manage hardware and software, develop IT products and services, predict market trends and customer behavior, target advertising, take care of cybersecurity, etc.

Financial Services

The second most popular industry for data scientists is financial services. It also offers plenty of different opportunities. For example, you can detect financial fraud, create and improve credit rating models, segment customers, predict their behavior, and improve pricing models. Data scientists are also needed in stock and FX trading, risk management, financial analysis, and so on.

Management & Consulting

This is an industry that includes various types of consulting services, such as financial, legal, tax, advertising, data and database management, or other IT services. You could be working on identifying historical patterns in the court’s rulings, targeting customers with advertising campaigns, managing other companies’ databases and creating the software solutions for them, analyzing their financial data and steering them in improving their business, etc.

Pharmaceutical & Biotechnology

When it comes to the pharmaceutical and biotechnology industry, data scientists are again highly in demand. Their skills are used to create predictive models on the molecular and clinical data that help in drug discovery and drug trials. Your skills are also needed in the market side of the business: you’ll analyze the market, discover your potential customers, and target advertising to them.

Health

Healthcare uses data science across clinical care, operations, and public health. You might build models for diagnosis support, risk stratification, readmission prediction, or medical imaging analysis; apply NLP to EHR notes; or optimise staffing, bed capacity, and patient flow. There’s also population health and claims analytics, fraud/waste detection, and personalisation for telehealth and patient engagement.

Retail & E-Commerce

Retail and online marketplaces rely on data scientists to forecast demand, set prices, and keep the right inventory in the right place. You could work on search and ranking, recommendation systems, and customer segmentation; measure marketing impact and reduce churn; or tackle fulfilment: supply-chain optimisation, delivery-time prediction, and returns/fraud detection. There’s also A/B testing and experimentation behind nearly every product decision.

Data Scientist Career Path

In this section, we’ll focus on the hierarchical level within the organization and show some specializations for each seniority. If you’re more interested in the actual job title details, it’s best that you take a look at 14 data science job titles. The data scientist’s career path usually starts as an individual contributor (IC).

The data scientist’s career path usually starts as an individual contributor (IC). The ICs are not responsible for managing anyone; they are employees carrying out the operational work. On this path, you can be:

• Analyst
• Associate

After the Associate level, you’ve gained a respectable amount of experience. Now it’s time for you to become a leader and start the Management Track. There are three positions available here:

• Manager
• Senior Manager
• Group Manager

After becoming a Group Manager, the career path branches off into two leadership tracks. While both relate to leadership positions, they are different.

The first one is the Management Leadership Track, which focuses on managing a team. The three levels are:

• Director
• Senior Director
• Vicepresident (VP)

The other leadership track is the IC Leadership Track. This one relates to managing large projects as an IC. These are the levels:

• Staff
• Principal
• Distinguished
• Fellow

We’ll now go through all the positions and talk about what they are, the required experience and skills, and what salary they offer. In addition, we’ll mention several specializations you can pursue at each seniority level. Before that, it’s essential that you understand what a data scientist does.

Here’s an overview of the data science career path seniority.

Individual Contributor

Typically, the most junior position is an analyst.

Analyst

They are full-fledged data scientists, but the ones with no or minimal experience. Those are usually fresh graduates holding a bachelor’s or master’s degree in quantitative fields, such as computer science, statistics, mathematics, etc.

At this level, you’re still not independent, and you’ll probably rely a lot on your senior colleagues to give you direction when you get stuck with something. They will also more often check your work and tell you how something could have been done (better).

You’ll also ‘embarrass’ yourself by making some serious (but not too serious!) mistakes. But there’s a reason why this is in inverted commas: you must learn from others, but also your mistakes; there’s nothing embarrassing in that.

Experience: 0-1 years

Salary: $82k - $141k

Skills:

• Core tools: SQL, Python, R
• Data wrangling: pandas, NumPy, data cleaning techniques
• Statistical thinking: hypothesis testing, distributions, confidence intervals
• Data visualization: Power BI, Tableau, Looker, Matplotlib, seaborn
• Basic ML: logistic regression, linear regression, scikit-learn pipelines
• Notebooks & version control: Jupyter, Git

Specializations

Depending on your interests and opportunities, you can start with specializations even at this most junior level.

Associate

After a year or so, you could expect to become an associate. Those are more experienced data scientists who are able to work more independently. They not only complete the particular task that is a part of a project, but they can also be operationally in charge of the whole project.

That includes scoping problems, exploring solutions, building and validating models, and presenting findings to cross-functional teams.

You’ll work more independently, and you are expected to think critically about model design trade-offs, data quality, and business value. You’ll also be more involved in experiment design, feature engineering, and sometimes light deployment.

The associates serve as mentors to the younger members of the team. Even though they’re not officially managing anybody, data science managers usually delegate to the associates directly, giving them a certain freedom to engage other (more junior) team members.

Experience: 1-4 years

Salary: $118k - $168k

Skills:

• Modeling & ML: logistic regression, decision trees, random forests, XGBoost, clustering, and intro to neural networks
• Experimentation: A/B testing design, statistical power, uplift analysis
• Feature engineering: text features, time-based features, aggregations, encoding techniques
• Data pipelines: designing reproducible workflows using scikit-learn pipelines or orchestration tools like Airflow or dbt
• ML tooling: exposure to tools like MLflow, Weights & Biases, and model registries
• Communication: writing clear experiment summaries, model caveats, and presenting trade-offs to stakeholders

Specializations

Here’s an overview of the specializations typical for this level.

Management Track

After four years of experience in data science, you’re now stepping into the management track.

Manager

At this level, you’re starting with the technical leadership. While you may still perform some operational work, your primary task will be managing a team of data scientists, ML engineers, and analysts. What does that include?

You will organize your team, delegate tasks, monitor your team’s performance, and set out strategies, especially in how AI/ML solutions align with business goals.

You’ll also be mentoring your team and be a bridge (but also a shield) between them and the management. This doesn’t mean only loading your team with the tasks coming from your superiors; it also means pushing back and protecting your team from shifting priorities and overload.

Management always boils down to trying to achieve your company’s goals and keeping your bosses happy while keeping your team happy, too.

Experience: 4-7 years and background in managing teams

Salary: $192k - $306k

Skills:

• Organizational leadership: team planning, resourcing, hiring, performance reviews
• Project management: ML roadmaps, backlog prioritisation, cross-team delivery (Jira/Linear)
• Technical guidance: experiment/evaluation standards, production trade-offs, model readiness (MLflow or Weights & Biases)
• Communication: clear RFCs, status updates, executive readouts
• Cross-functional collaboration: align KPIs/SLAs with product, engineering, and infra (Tableau/Looker/Power BI)
• Mentorship & team growth: coaching ICs, feedback, development plans
• Process ownership: code reviews, CI/CD, sprint cycles, postmortems (GitHub/GitLab CI; Airflow; Datadog/Prometheus)

Specializations

At the manager level, you typically have these opportunities for specialization.

Senior Manager

The main difference between the manager and the senior manager is the experience, particularly in management. Senior managers could also lead several teams, each having its own manager. You’re not only managing projects; you’re also managing managers and broader AI/ML strategy across teams.

This seniority is also concerned with AI ethics, model governance, and cross-functional dependencies.

Senior managers are in charge of hiring, firing, and budgeting for their team(s).

This is usually the last job position in data science before you go into one of the leadership tracks.

Experience: 5-7 years and background in managing teams

Salary: $207k - $325k

Skills:

• Org-level leadership: managing managers, budgets, multi-team planning
• Strategic alignment: turning company goals into AI/ML roadmaps and OKRs (Workboard or Viva Goals)
• Hiring and retention: growing cross-functional, specialised ML teams
• Tooling and infrastructure oversight: evaluating platforms, driving standardisation (Databricks/Snowflake; Kubernetes/Terraform; Airflow/dbt; MLflow)
• Ethics & compliance: enforcing responsible AI, documentation, auditability (Fairlearn; Great Expectations; DataHub/OpenLineage)
• Stakeholder influence: communicating AI strategy and impact to execs/partners (Tableau/Looker/Power BI)

Specializations

Here are several specialization options at the senior manager level.

Group Manager

The last position in the Management Track is a Group Manager. This is typically a role that exists only in larger data science companies. Smaller companies don’t need the Group Manager position.

As a Group Manager, you’ll be managing one or several teams or a division, e.g., Applied ML, MLOps, or AI Infrastructure. You will be in charge of setting out the strategic goals, managing and directing your team in reaching those goals, and ensuring the day-to-day operations. This includes balancing long-term R&D efforts (e.g., foundation model exploration) with short-term delivery (e.g., ML productization).

The job description includes monitoring the performance of your team/division, hiring, educating them, and developing their careers. You’ll also be responsible for budgeting and keeping your operations within budget.

Experience: Typically 7–10+ years, including team-of-teams management and cross-org alignment.

Salary: $233k - $390k

Skills:

• Strategic planning: org-wide OKRs, portfolio priorities, roadmaps (Viva Goals or Workboard)
• Organizational design & collaboration: structuring centralised vs embedded teams; RACIs; cross-functional rhythms
• AI governance & lifecycle ops: standardize tracking, lineage, testing, bias reviews (MLflow or W&B; DataHub/OpenLineage; Great Expectations; Fairlearn)
• Talent management: hire/retain across ML, MLOps, AI Research; growth frameworks and succession
• Budgeting & vendor management: cloud/LLM contracts, cost control, usage SLAs (AWS/GCP/Azure cost tools; Databricks/Snowflake usage dashboards)

Specializations

Here are several specializations available at the Group Manager seniority.

Management Leadership Track

After reaching the Group Manager position, one of the opportunities for a promotion is the Management Leadership Track. It’s a career path where you’re more dedicated to managing people and the company than applying your data science expertise.

Director

It’s a strategic position mainly focused on developing and implementing policies, procedures, and goals for the whole data science function. This position also deals with creating processes and the entire data science framework, including data architecture, data collection methodologies, storing, distribution, and use. At this level, you’re ultimately shaping how data and machine learning contribute to company-wide goals.

Also, you’re defining framework and processes across the data lifecycle, from architecture and ingestion to model deployment, monitoring, and ROI measurement.

The teams you manage are typically cross-functional and include MLOps, applied science, data engineering, and analytics, especially in ML-heavy companies.

The Director communicates with the senior management and works on putting the data science to the best use for achieving the company’s strategic goals.

Experience: 7-10 years, including technical leadership across multiple teams.

Salary: $253k - $432k

Skills:

• Strategic AI/ML leadership: roadmaps for applied ML, AI products, and automation initiatives, portfolio/OKRs (Workboard or Viva Goals)
• Tech stack leadership: cloud infrastructure (AWS/GCP/Azure), big data platforms, modern data stack (Spark, dbt, Snowflake)
• ML production maturity: CI/CD, tracking, observability, governance (MLflow; Airflow; Kubernetes; Prometheus/Grafana)
• Cross-functional collaboration: interfacing with C-suite, product, infra, and legal/data compliance
• Organizational design: structuring data science teams, defines roles (MLOps, research, applied), sets org-wide best practices

Specializations

Typical specializations you can undertake at this level are given in the table below.

Senior Director

The Senior Director is all about setting directions for entire functions across AI/ML, data science, and engineering.

You keep oversight of multiple directors below you, along with managing relationships with executive leadership, external partners, and key stakeholders.

If you’re in this position, you need to show really strong business acumen and understanding of your competitors and customers. You need to be up-to-date with the latest business and technology trends, especially in AI, such as generative models, edge deployment, and AI regulation.

Experience: 10+ years with a background in managing large cross-functional or multi-disciplinary teams.

Salary: $287 - $502k

Skills:

• Strategic AI/ML foresight: long-range vision for AI products, research bets, and platform innovation (Viva Goals or Workboard)
• Cross-functional alignment: connect Research, Engineering, Product, and Exec leadership on org-wide priorities and trade-offs
• Industry awareness: track AI/ML trends, competitor moves, and regulation; adjust strategy and risk posture
• Executive stakeholder management: own communication with board-level stakeholders, partners, and key external collaborators
• AI governance leadership: set frameworks for responsible AI, safety, and impact measurement (NIST AI RMF; Model Cards; MLflow/W&B for traceability; DataHub/OpenLineage)

Specializations

Several common Senior Director specializations are shown below.

VP

Being the Vice President (VP) requires you to be even more customer-centric than the Senior Director. They often serve as the face of the company, not only towards the customers but also other stakeholders such as investors, regulators, etc. This means they need to have highly developed presentation skills and be good at public relations.

They are not directly involved in managing teams, but they manage other directors and managers leading teams, defining the company’s AI/ML strategy at the highest levels. Together with C-level executives, they align product roadmaps, M&A strategy, and responsible AI policies to translate the strategy into long-term growth. For that, they need a visionary mindset.

The VPs often have the power to sign agreements and internal decisions.

Salary: $324k - $543k

Experience: 15+ years, including org-level leadership and AI/ML delivery at scale

Skills:

• Executive visibility: campaigning AI strategy to board, investors, and external partners; impact dashboards (Tableau/Looker)
• Organization-wide alignment: connecting AI/ML vision to business, product, engineering, and policy (Workboard or Viva Goals)
• Strategic foresight: anticipating AI market shifts and helping shape future-proof org structures; scenario planning (CB Insights)
• Influence & storytelling: communicating complex AI topics clearly to non-technical stakeholders
• External partnerships: leading collaborations with academia, vendors, regulators, and alliances

Specializations

Here are some VP specializations available.

IC Leadership Track

If you choose this leadership track, you won’t have your standard team to manage day-to-day. Instead, you’ll have to manage data science projects and act as an advisor. Your responsibilities in doing that depend on your seniority.

Staff

Even though this is a leadership role, you’ll spend some time coding. This, however, takes the smallest portion of your time. Most of your time is spent leading complex AI/ML or data science projects, including technical planning, documentation, and cross-functional coordination. You’re expected to drive execution and make high-impact architectural decisions.

You’ll also be mentoring data scientists and engineers, and be a guide for the project members, including technical advice, mentoring, and coaching.

You’ll act as a bridge between the employees doing the operational work and the company management, and report on your project(s) status directly to the management.

Experience: 5-7 years

Salary: $194k - $304k

Skills:

• Project and architecture ownership: scoping and leading large-scale ML or data infra projects using tools like Kubernetes, MLflow, Airflow, or Ray for orchestration and tracking
• Technical leadership: expert-level knowledge in model development (e.g., with PyTorch, TensorFlow, XGBoost) and data pipelines (e.g., Spark, dbt, Kafka) across multiple domains
• Cross-functional coordination: partnering with infra (e.g., platform teams using Terraform/GCP/AWS), product managers, and research scientists to align goals
• Mentorship: reviewing ML/DS design docs and code, helps shape careers, and scales knowledge through internal training or standards docs
• Strategic influence: informing decisions on tools like Feast (feature stores), KServe/Triton (model serving), Weights & Biases, or Databricks; balances trade-offs in scalability, reproducibility, and cost

Specializations

These are examples of specializations at the staff seniority level.

Principal

Principals are at the intersection of technical depth and business strategy. They guide senior leadership on the long-term direction of AI/ML capabilities and ensure technical decisions align with product vision, company goals, and regulatory constraints.

They influence architectural direction, assess the risk of AI systems, and weigh trade-offs in platform design, ML deployment, and research investments.

The Principals also provide a high-level perspective for the project teams. You can understand the position as an advisory position to them and the management at the same time.

Experience: 7-10 years

Salary: $214k - $358k

Skills:

• AI/ML architecture strategy: set cross-org standards for training, deployment, monitoring (MLflow; Kubeflow/Kubernetes)
• Business-aligned technical planning: target LLMs/RAG/forecasting/real-time where they move metrics; make build-vs-buy calls (Vertex AI or SageMaker)
• Organization-wide influence: authoring RFCs and reviewing design docs across stacks like dbt, Apache Spark, Airflow, Ray, or Lakehouse architectures using Delta Lake/Iceberg
• Stakeholder management: communicating roadmap, limits, and risk trade-offs; establishing responsible-AI guardrails
• Risk and impact forecasting: defining failure modes and observability at scale (Evidently; Prometheus/Grafana)

Specializations

Here are some specialization examples.

Distinguished

Distinguished contributor is also an advisory role but even more focused on long-term AI/ML strategy and innovation that the Principal. You advise executives on emerging technologies, model architectures, and infrastructure directions.

This role is at the intersection of research, product, and industry foresight. You collaborate with academia, vendors, and AI research leaders to anticipate shifts in foundational models, compute architectures, and deployment paradigms.

Salary: $251k - $434k

Experience: 10+ years

Skills:

• Visionary leadership: identifying AI/ML inflection points before they hit industry adoption
• External influence: working with researchers, open-source projects, and conferences to shape AI discourse (Hugging Face Hub; GitHub; arXiv)
• Strategic foresight: advising on platform direction, model governance, and emerging AI infrastructure (Kubernetes; Ray; Databricks or Snowflake; MLflow)
• Research integration: bridging frontier research (LLMs, AGI precursors, new modalities) into product strategy (PyTorch; JAX; Hugging Face Transformers; Triton/ONNX Runtime)
• Executive advising: informing C-suite on long-term AI investments, risks, and competitive positioning

Specializations

Here are some positions you can specialize in as a distinguished contributor.

Fellow

Becoming a Fellow is the highest position you can reach in the IC Leadership Track, and it’s analogous to the VP in the Management Track.

Fellows usually possess internationally recognized knowledge on AI/ML, often with deep specialisation in fields like large language models (LLMs), generative AI, multi-modal systems, or causal inference.

Their expertise has a significant impact on the company's long-term AI/ML research and product direction. They often come from academia and research and are mentors to other positions in IC Leadership Track.

Their influence extends beyond the organisation, guiding industry standards and mentoring leaders across all IC levels.

Experience: 15+ years

Salary: $312k - $575k

Skills (in addition to Distinguished):

• Research leadership: pushing state of the art AI/ML via publications, patents, and OSS (arXiv, GitHub)
• Technical authority: leading work in generative modelling, advanced neural architectures, AI alignment (PyTorch or JAX; CUDA/TPU; DeepSpeed/Megatron-LM)
• Industry influence: shaping standards via consortiums (e.g. MLCommons, Hugging Face’s BigScience), keynote speaking, or advisory boards
• Mentorship at scale: training the next generation of senior ML engineers, scientists, and architects
• Strategic alignment: steer long-range AI bets tied to platform and product (MLflow or W&B; Databricks or Snowflake; OpenAI or Vertex AI)

Specializations

Here are several specializations at this level.

Conclusion

As you saw from the data scientist career path we presented here, you have plenty of ways to steer your data science career. With experience, you get more freedom to choose.

The data scientist career path doesn’t only offer you the opportunity to forever deal with operative tasks. You can, if you want, of course. But this data science career path also gives you the move into management, where you’ll manage teams and run the company.

Maybe management positions are not something you want. Instead, you can choose to remain the IC. This leadership path has four levels and generally keeps you in your data science profession but moves you from groundwork to an advisory role. While you won’t be running a company directly, you’ll be able to impact the company’s direction and run it indirectly.