Data Scientist

Location: Boston, Chicago, Los Angeles, New York, or San Francisco

About the role

We are actively looking for a Data Scientist to join L.E.K.’s rapidly growing DDA (Data, Digital, and AI) practice. This role sits within our Data Science & Engineering team and operates at the intersection of advanced analytics, machine learning, and applied AI, building the models, applications, and analytical products that power client engagements and L.E.K.'s proprietary IP.

The ideal candidate brings fluency in applied data science (e.g., model development and validation, experimentation, and deployment of models) alongside hands-on experience building and shipping LLM-based and agentic AI solutions. You will work directly with clients across multiple industries, translating complex technical work into measurable commercial outcomes, and partner with data scientists, strategy consultants, and client teams to build solutions that create high client impact. As part of a dynamic and growing team, there is every opportunity to carve out value-added work and grow your skills and experience.

Responsibilities

• Client engagements:
  • Support end-to-end data science projects from conceptualization through to deployment, and deploy advanced machine learning models in clients' cloud environments, optimizing for scalability, performance, and reliability to address specific business challenges and objectives.
  • Support clients in strategically leveraging technical models, guiding them through the interpretation of results and the integration of actionable insights into their business workflows.
  • Solve a wide variety of complex analytical challenges for clients, sometimes dynamically balancing multiple client engagements at one time.
  • Deliver across the full analytical stack: data aggregation and creation, cleaning and manipulation, commercial data science (geospatial, machine learning, predictive modeling, NLP, LLMs), and visualization.
  • Guide clients through the interpretation of analytical outputs and integration of data-driven insights into their business workflows.
• Client / business development:
  • Support Managing Directors in developing and scoping client proposals where data science, ML, and AI capabilities are central to delivery.
  • Serve as the technical interface between the Data Science & Engineering team, consulting partners, and clients while ensuring alignment on fit, feasibility, and delivery expectations.
  • Translate technical requirements, outputs, and constraints into clear, actionable language for client-facing presentations and proposals.
  • Support the design and commercialization of new offerings across data science, ML, agentic AI, and LLM-powered analytics.
• Capability development:
  • Contribute to the development of state-of-the-art analytical apps, leveraging up-to-date machine learning algorithms to solve complex problems.
  • Collaborate with a variety of stakeholders to continuously innovate on the apps, service lines, and proprietary data assets we can offer.
  • Provide technical expertise and thought leadership on developing analytical tools, service lines, and proprietary data assets, and contribute to building these areas directly when applicable.
  • Uphold best-in-class standards in app development, software, data integrity, and ensuring solutions are both scalable and maintainable.
  • Support commercialization and upskilling of staff on relevant software, tools, and techniques.

Qualifications

• Data science:
  • A minimum of 2 years of experience in applied data science with a solid foundation in machine learning, statistical modeling, and analysis.
  • Strong knowledge, experience, and fluency in a wide variety of tools including Python with data science and machine learning libraries (e.g., scikit-learn, TensorFlow, PyTorch), Spark, SQL.
  • Technical understanding of machine learning algorithms; experience with deriving insights by performing data science techniques including classification models, clustering analysis, time-series modeling, NLP; technical knowledge of optimization is a plus.
  • Expertise in developing and deploying machine learning models in one of the cloud environments (AWS, Azure, GCP) with an understanding of cloud services, architecture, and scalable solutions (e.g., Sagemaker, Azure ML, Kubernetes, Airflow).
  • Demonstrated experience with MLOps practices, including continuous integration and delivery (CI/CD) for ML, model versioning, monitoring, and performance tracking to ensure models are efficiently updated and maintained in production environments.
  • Exposure to design, develop, and deploy agentic AI systems (e.g., multi-agent orchestration or automation workflows) using frameworks such as LangChain.
  • Hands-on experience with manipulating and extracting information on a variety of large both structured and unstructured datasets; comfort with best data acquisition and warehousing practices.
  • Proficient in Excel, PowerPoint, and excellent communication skills, both written and oral.
• Stakeholder engagement:
  • Ability to understand and articulate requirements to technical and non-technical audiences, working alongside data science, engineering, and consulting teams.
  • Experience with commercial business analytics and in strategic consulting is preferred; exposure to one or more of L.E.K.’s core sectors (life sciences, healthcare, consumer, industrials, TMT) is a strong plus.
  • Strong problem-solving skills with the ability to translate business needs into technical solutions; comfortable working at pace in a fast-paced, entrepreneurial environment with a high degree of ownership.
  • Ability to achieve results through others; experience and proven success record working in matrix, agile, and fast-growing environments; assertive, intellectually curious, and continuously driving towards excellence.
• Education:
  • Degree in a quantitative and/or business discipline preferred, examples include: Statistics, Computer Science, Data Science, Mathematics, Operations Research, Engineering, Economics.

Benefits

L.E.K. Consulting offers a competitive total rewards package including:

• Medical, dental, vision, life, and disability insurance.
• 401(k) with employer contribution.
• HSA contributions (where applicable).
• Paid time off and other firm-sponsored benefits.

Pay

The expected base salary for this position is $110,000 - $120,000 annually. Actual compensation will be determined based on experience, qualifications, skills, and location. This position may also be eligible for a discretionary bonus.

Schedule

This role follows our hybrid work model for U.S. offices. We require employees to be in their assigned home office Tuesday, Wednesday, and Thursday each week, as well as the first Friday of each month.