Data Scientist — AI, LLMs & Cloud Analytics

L.E.K. Consulting; Boston (MA); Hybrid; USD 110,000 - 120,000; Full time

Benefits offered by this job

Discretionary bonus

401(k) with employer contribution

HSA contributions

Job summary

L.E.K. Consulting is seeking a Data Scientist to join the DDA practice within the Data Science & Engineering team in a hybrid role based in Boston, MA.

You will help deliver end-to-end analytics and machine learning, including LLM-based solutions, across client engagements and L.E.K.’s proprietary IP. Take data science work from conceptualization through deployment, deploying models in clients’ cloud environments with emphasis on scalability and reliability, and support interpreting results to

Qualifications

• 2+ years of applied data science with ML foundation.
• Proficiency in Python, Spark, SQL and ML libraries.
• Experience deploying ML models in cloud environments.
• Competence with MLOps, CI/CD and model monitoring.
• Ability to communicate across technical and non-technical audiences.
• Prior exposure to AI/LLM-based analytics and data warehousing.

Responsibilities

• Develop and deploy ML models with focus on scalability and reliability.
• Interpret results and embed insights into client workflows.
• Handle multiple engagements and complex analytics tasks.
• Build data apps and visuals using current ML approaches.
• Guide proposals and ensure alignment with delivery teams.

Skills

2+ years

Python

Spark

SQL

MLOps

LangChain

AWS/Azure/GCP

CI/CD

Excel

PowerPoint

Education

Quantitative degree (Statistics, CS, DS, Math, OR, Engineering, Economics)

Tools

Sagemaker

Azure ML

Kubernetes

Airflow

LangChain

Excel

PowerPoint

Job description

L.E.K. Consulting is seeking a Data Scientist to join the DDA practice within the Data Science & Engineering team in a hybrid role based in Boston, MA.

You will help deliver end-to-end analytics and machine learning, including LLM-based solutions, across client engagements and L.E.K.’s proprietary IP. Take data science work from conceptualization through deployment, deploying models in clients’ cloud environments with emphasis on scalability and reliability, and support interpreting results to