Description :

Overview :

The Senior Data Scientist is responsible for designing and delivering machine learning solutions using the organizations on-premises infrastructure. This role covers the full lifecycle of ML development, including data exploration, feature engineering, model training, validation, deployment, and monitoring. The position requires strong analytical skills, hands-on modeling experience, and the ability to communicate insights effectively to technical and business teams.

Key Responsibilities :

- Analyze large volumes of structured and unstructured data to identify patterns and opportunities.

- Build high-quality features using Python, SQL, and Spark in collaboration with Data Engineering teams.

- Validate data integrity, identify anomalies, and ensure consistent input data for models.

- Develop predictive models using regression, classification, clustering, time-series forecasting, and other ML techniques.

- Build deep learning models when required, using frameworks such as TensorFlow or PyTorch.

- Evaluate and compare models using appropriate statistical and performance metrics.

- Use on-prem MLOps tools (e.g., ClearML, Dataiku) for experiment tracking, dataset versioning, and model management.

- Deploy models into production using container-based environments and internal compute/GPU infrastructure.

- Work with engineering teams to integrate models with business applications and data pipelines.

- Monitor model performance, drift, and data quality over time.

- Update, retrain, or recalibrate models to ensure continued accuracy and business value.

- Maintain clear documentation for models, datasets, methodologies, and deployment procedures.

- Translate complex analytical results into actionable insights for business stakeholders.

- Participate in requirement workshops to define use cases, success criteria, and expected outcomes.

- Provide guidance to data analysts and junior data scientists.

Required Skills & Experience :

- 6-10 years of experience in applied machine learning, data science, or advanced analytics.

- Strong proficiency in Python, SQL, and ML frameworks (scikit-learn, TensorFlow, PyTorch).

- Hands-on experience building and deploying models on on-prem environments.

- Strong foundation in statistics, probability, experimental design, and model validation.

- Ability to work with large datasets and distributed computing environments.

- Strong communication and documentation skills.

- Experience with GPU-accelerated model training and optimization.

- Knowledge of model serving frameworks and containerized inference.

- Exposure to vector databases, NLP, or deep learningbased feature extraction.

- Experience collaborating with enterprise data engineering teams.