Sr. data scientist lead

Position: Sr. Data Scientist Lead Experience: 12+ years We are looking for a Senior Data Scientist Lead to drive the design and delivery of advanced analytics and machine learning solutions across client engagements in Medical Tech, Oil & Gas, Retail, and Banking. This is a senior technical leadership role responsible for owning the end-to-end data science lifecycle — from problem framing and data strategy through model development, validation, and production deployment. The ideal candidate combines deep hands-on expertise in statistical modeling and ML with the ability to mentor data science teams and drive analytics strategy across multiple client verticals. Key Responsibilities Own end-to-end data science workflows — from business problem framing, data exploration, and feature engineering through model development, validation, and deployment. Design and build predictive, statistical, and ML models (classification, regression, forecasting, clustering, NLP, recommendation systems) tailored to client business problems. Lead model experimentation and evaluation, optimizing for accuracy, interpretability, latency, and scalability trade-offs in production settings. Define reference architectures for data pipelines, feature stores, and model deployment across diverse client data ecosystems. Set technical direction and best practices for the data science function across client engagements in Medical Tech, Oil & Gas, Retail, and Banking. Partner with client stakeholders, project managers, and engagement leads to translate business requirements into analytics roadmaps. Mentor and technically guide data scientists and analysts; conduct model and code reviews. Evaluate and select tools, platforms, and cloud infrastructure for data science workloads. Own MLOps practices: model versioning, monitoring, retraining, and CI/CD for ML systems. Stay current with advances in data science and ML, and drive adoption of relevant new techniques and tools. Required Skills & Experience Experience 12+ years in data science/analytics, with a substantial portion in applied ML and statistical modeling. Demonstrated experience leading data science projects from problem definition through production deployment. Data Science & ML Strong hands-on expertise with the data science stack — Python (pandas, scikit-learn, NumPy), SQL, and statistical modeling techniques. Experience building and deploying ML models: data curation and feature engineering, model selection, hyperparameter tuning, and validation frameworks. Familiarity with deep learning frameworks such as PyTorch and/or TensorFlow, and experience with NLP, time-series forecasting, or recommendation systems. Data Engineering & Pipelines Experience working with large-scale structured and unstructured data, data warehousing, and ETL/ELT pipelines. Proven ability to design and build end-to-end analytics pipelines: ingestion, transformation, feature stores, model serving, and reporting/BI delivery. Experience with big data frameworks (e.g., Spark, Databricks) and visualization/BI tools (e.g., Tableau, Power BI). Engineering & Leadership Strong software engineering fundamentals in Python, system design, and experience productionizing models at scale. Experience with cloud platforms (AWS/Azure/GCP) and containerization/orchestration (Docker, Kubernetes). Track record of technical leadership, mentoring data science teams, and driving analytics strategy across multiple projects or clients. Strong communication skills, with experience engaging directly with client stakeholders and presenting to business/executive audiences. Qualifications & Preferred Attributes Bachelor's or Master's degree in Data Science, Statistics, Computer Science, or a related field (PhD a plus). Prior experience in a Lead/Principal/Staff Data Scientist role. Domain experience in one or more of: Medical Tech (clinical analytics), Oil & Gas (predictive maintenance, sensor analytics), Retail (customer analytics, demand forecasting), or Banking (risk/fraud analytics) is a strong plus. Experience working in a consulting or client-services environment, managing multiple concurrent engagements. Publications, patents, or open-source contributions in data science/ML are a plus. Willingness to travel internationally for client engagements as needed.