Data Science Operations Lead

Location: Cambridge

Role Description s:
We are seeking a seasoned Data Science Operations Lead to spearhead our AI/ML and data science operations. This leadership role requires a hands-on technologist with 12+ years of experience who can drive the end-to-end lifecycle of data science and machine learning initiatives from ideation and experimentation through production deployment and ongoing operations at enterprise scale.

The ideal candidate combines deep technical expertise in AWS cloud (including AWS Data Lake architectures), Databricks, and big data processing with strong leadership and stakeholder management skills honed in large, multinational organizations operating in onshore/offshore delivery models.

Experience with Generative AI is a strong differentiator.

Key Responsibilities

Leadership & Strategy

Lead and manage a team of 6+ data scientists, ML engineers, and analytics professionals across onshore/offshore locations, providing technical mentorship and career guidance.

Define and drive the data science operations strategy, roadmap, and best practices aligned with business objectives.

Partner with senior business stakeholders, product owners, and cross-functional teams to identify high-impact AI/ML opportunities and translate them into actionable project plans.

Establish and govern standards for model development, deployment, monitoring, and responsible AI adoption across the organization.

Hands-On Technical Delivery

Architect and oversee scalable ML pipelines for data ingestion, feature engineering, model training, validation, and inference on AWS cloud and Databricks.

Design and implement AWS Data Lake architectures and big data processing solutions for structured and unstructured data at petabyte scale using Spark, Databricks, and AWS-native services (S3, Lake Formation, EMR, Glue, Sage Maker, Redshift, Athena).

Lead the deployment of production ML systems including real-time inference APIs, batch prediction pipelines, and model-as-a-service architectures.

Drive MLOps maturity CI/CD for ML, automated model retraining, drift detection, A/B testing, and performance monitoring.

Data Science & Advanced Analytics

Oversee and contribute to data science projects spanning exploratory data analysis, statistical modeling, machine learning, and NLP.

Guide the team in applying advanced techniques classification, clustering, regression, time-series forecasting, and deep learning to solve complex business problems.

Champion the adoption of Generative AI capabilities (LLMs, RAG architectures, prompt engineering) for advanced analytics, automation, and solution prototyping.

Operational Excellence

Establish frameworks for model governance, reproducibility, version control, and compliance in enterprise settings.

Monitor model performance, reliability, and retraining cycles to ensure sustained business value.

Build reusable frameworks, templates, and internal tools that accelerate team delivery velocity.

Must-Have Qualifications

Experience

12+ years of progressive experience in data science, machine learning engineering, or related fields, with at least 3 4 years in a leadership/lead role.

Proven track record of delivering scalable, production-grade ML/AI solutions in large, multinational organizations.

Experience managing a team of 6+ resources with demonstrated ability to coordinate across onshore/offshore delivery models.

Strong experience mentoring technical teams and driving accountability across distributed locations.

Technical Skills

AWS Cloud & Data Lake (Expert):
Deep hands-on experience with AWS services Sage Maker, S3, EMR, Glue, Lambda, Redshift, Athena, Step Functions, Lake Formation, and IAM/security best practices. Proven experience designing and managing AWS Data Lake architectures.

Databricks (Expert):
Proficiency in Databricks for large-scale data engineering, ML model development, MLflow for experiment tracking, and Unity Catalog for governance.

Big Data Processing (Expert):
Strong experience with Apache Spark (PySpark/Scala), distributed computing, and processing large-scale structured/unstructured datasets.

Machine Learning:
Solid expertise in ML algorithms, feature engineering, model evaluation, and frameworks such as scikit-learn, XGBoost, Tensor Flow, or PyTorch.

MLOps:
Proven experience with end-to-end ML lifecycle model deployment, monitoring, retraining, CI/CD pipelines, containerization (Docker), and orchestration (Kubernetes/ECS).

Python:
Expert-level proficiency for end-to-end data science and ML workflows.

SQL:
Advanced SQL for data analysis and pipeline development.

Code Management:
Proficiency with Git, branching strategies, and code review practices.

Soft Skills

Excellent communication and the ability to present complex technical concepts to senior business stakeholders.

Strong project management skills with experience driving cross-functional initiatives.

Inquisitive, problem-solving mindset with a focus on measurable business impact.

Good-to-Have Qualifications

Generative AI:
Hands-on experience with LLM-based applications, prompt…