Data Science & Engineering Lead

Role overview Lead hands-on data science and engineering work, from developing machine learning models to building the systems that support them in production. The scope spans modeling, data platforms, cloud infrastructure, MLOps, and business intelligence, alongside mentoring junior team members. The role is remote and based in India.

Responsibilities - Design and lead supervised and unsupervised models for business problems, including statistical, ensemble, and neural network approaches. - Direct work across NLP, computer vision, forecasting, and anomaly detection; apply techniques such as retrieval-augmented generation where suitable. - Build scalable batch and streaming pipelines and lakehouse architectures, including layered data processing. - Lead ETL development and optimize relational and NoSQL data models for transactional, analytical, and unstructured data. - Guide cloud infrastructure and production MLOps workflows, and develop BI dashboards for actionable reporting. - Mentor junior colleagues in machine learning, AI, and engineering practices.

Requirements - Strong experience developing and deploying machine learning models across multiple problem types. - Ability to design data pipelines, lakehouse solutions, and ETL workflows. - Knowledge of relational and NoSQL database modeling for operational and analytical use. - Experience with cloud infrastructure and production model deployment or MLOps. - Ability to lead technical work and mentor team members. - Familiarity with BI dashboard development and model training optimization.

Nice to have - Experience with tools and platforms including Spark, Databricks, Airflow, dbt, Airbyte, Snowflake, AWS, Azure, SageMaker, or Azure ML. - Exposure to GPU acceleration and CUDA for model training.