Data Scientist

Capgemini • New York, NY

10d • $94,248 - $215,050

About The Position

Choosing Capgemini means choosing a company where you will be empowered to shape your career in the way you’d like, where you’ll be supported and inspired by a collaborative community of colleagues around the world, and where you’ll be able to reimagine what’s possible. Join us and help the world’s leading organizations unlock the value of technology and build a more sustainable, more inclusive world.

Successful execution of Digital Lean requires helping lead a fundamental shift in how technology services are delivered. This role sits at the forefront of that change—driving a move toward an AI‑driven, product‑first mindset that uses data to anticipate issues, empower end users, and resolve problems before they escalate. As a full‑stack data scientist, you will turn complex operational signals into insight—and translate that insight into lightweight products, prototypes, and tools that reshape service delivery at scale.

Positioned at the intersection of data science, GenAI, and product thinking, you will work directly with product and delivery teams to surface non‑obvious opportunities, establish a clear point of view, and help convert ideas into durable, intelligent solutions that continuously improve how work gets done.

This is not a pure research role and not a traditional software engineering role. We are looking for a builder‑minded data scientist who uses modern AI tools to create, edit, and evolve code; rapidly prototype workflows and products; and partner closely with engineers, subject‑matter experts, and operators to move from insight to sustained impact.

Requirements

• 5+ years of experience in data science, analytics, or applied machine learning, ideally in operational, process, or product‑adjacent environments.
• Bachelor’s, Master’s, or Ph.D. in a quantitative or computational field (e.g., Computer Science, Statistics, Applied Mathematics, Operations Research, Engineering, or similar).
• Strong hands-on capability with data science tools (e.g., Python, SQL, notebooks, visualization tools) and comfort working end‑to‑end from raw data to insight.
• Deep analytical instincts—you are driven to ask better questions, challenge assumptions, and uncover non‑obvious relationships in data.
• Comfort with GenAI tools and techniques, using them to explore, prototype, and accelerate analysis and lightweight development (not as a black box, but as a collaborator).
• Ability to create and modify code with the help of AI, even if you are not a full-time software engineer.
• Strong grounding in statistics, machine learning, and model interpretation, with good judgment about when each is appropriate.
• Exceptional communication skills—able to clearly articulate insights, tell a compelling story with data, and help others internalize and act on your point of view.
• Demonstrated track record of delivering measurable business or operational impact through data‑driven work.
• Experience with process analytics, operational telemetry, or large‑scale enterprise data.
• Exposure to product analytics, internal tool-building, or analytics‑driven product development.

Responsibilities

• Transform raw operational data into insight-ready datasets, working across structured and unstructured sources (process data, logs, documents, tickets, free text).
• Interrogate process and operational data to surface inefficiencies, patterns of waste, bottlenecks, rework, and systemic failure modes that are not obvious at first glance.
• Find the nuggets that scale—novel, repeatable insights that go beyond one-off analysis and can be generalized across teams, accounts, or platforms.
• Conduct deep-dive analyses using statistical methods, machine learning, and modern GenAI techniques to uncover root causes, anomalies, and opportunity spaces.
• Leverage GenAI as a force multiplier to explore data, generate hypotheses, create and edit code, accelerate prototyping, and rapidly iterate on analytical approaches.
• Embed with product and delivery teams to help turn insights into prototype tools, workflows, dashboards, or decision aids that can evolve into durable products.
• Translate analytical insight into a clear narrative—connecting data to business impact, operational outcomes, and a compelling vision for scale.
• Influence without authority by helping others see what you see: clearly communicating findings, framing the problem, and aligning stakeholders around action.
• Participate in continuous improvement activities (e.g., goal deployment, Kaizen-style initiatives) to identify where data and tooling can accelerate learning and results.
• Quantify impact by tying insights to measurable outcomes such as efficiency gains, cost reduction, cycle time improvement, or quality uplift.
• Document patterns, methods, and learnings to enable reuse and establish best practices across the broader data science and analytics community.

Benefits

• Paid time off based on employee grade (A-F), defined by policy: Vacation: 12-25 days, depending on grade, Company paid holidays, Personal Days, Sick Leave
• Medical, dental, and vision coverage (or provincial healthcare coordination in Canada)
• Retirement savings plans (e.g., 401(k) in the U.S., RRSP in Canada)
• Life and disability insurance
• Employee assistance programs
• Other benefits as provided by local policy and eligibility

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: Ph.D. or professional degree