We are looking for a Senior Data Scientist with deep technical expertise in machine learning, statistical modeling, and large-scale data processing to build, deploy, and maintain production-grade analytics and AI solutions. This role focuses on applying advanced analytics to machine and engineering data across the full product lifecycle, from development and validation to performance optimization and predictive maintenance.

Key Technical Responsibilities :

Data Engineering & Analysis :

- Ingest, clean, and preprocess large-scale structured and unstructured datasets including telemetry data, test fleet data, and test bench data.

- Perform advanced exploratory data analysis (EDA) to identify anomalies, trends, correlations, and failure patterns.

- Implement robust data validation, feature engineering, and data quality checks aligned with governance standards.

Statistical Modeling & Machine Learning :

- Design, develop, and deploy statistical and ML models including linear and non-linear regression, logistic regression, time-series forecasting, clustering, and classification.

- Develop deep learning models for sequence-based and high-volume data (e.g., LSTM/GRU, CNNs, transformers where applicable).

- Build predictive models for maintenance forecasting, issue detection, usage pattern analysis, and performance optimization.

- Select and evaluate algorithms using appropriate metrics, cross-validation, and bias/variance analysis.

Model Lifecycle & MLOps :

- Own the end-to-end model lifecycle : problem formulation, experimentation, POC validation, production deployment, monitoring, and retraining.

- Implement model performance tracking, drift detection, and automated retraining strategies.

- Package models for scalable deployment using cloud-native and distributed ML pipelines.

Big Data & Cloud Platforms :

- Develop analytics workflows using Apache Spark / PySpark, Hive, and distributed compute environments.

- Deploy and manage models using Databricks, AWS Glue, and Amazon SageMaker.

- Optimize pipelines for performance, scalability, and cost efficiency.

Collaboration & Technical Leadership :

- Work closely with data engineers, cloud engineers, and platform teams to integrate ML solutions into production systems.

- Translate business and engineering problems into mathematical formulations and computational workflows.

- Provide technical mentorship to junior data scientists, including code reviews, model design guidance, and best practices.

Visualization & Communication :

- Develop technical dashboards and visualizations using Dash, Tableau, or Shiny to communicate model results and system behavior.

- Clearly document assumptions, methodologies, and limitations of models for technical and non-technical stakeholders.

Technical Skills & Qualifications :

Required :

- 8+ years of experience in data science, machine learning, or applied analytics.

- Strong programming skills in Python; advanced use of SQL and PySpark.

- Hands-on experience with ML frameworks : scikit-learn, TensorFlow, PyTorch.

- Solid foundation in statistics, probability, optimization, and numerical methods.

- Experience with time-series analysis, predictive modeling, and large-scale datasets.

- Proven experience deploying models into production environments.

- Strong understanding of data governance, versioning, and reproducibility.

Preferred :

- Experience with unstructured data, NLP, LLMs, or topic modeling.

- Familiarity with CI/CD for ML, experiment tracking, and feature stores.

- Experience working with industrial, IoT, or machine-generated data.

- Academic background in Mathematics, Statistics, Data Science, Engineering, or related fields.