Role Overview :

We are seeking a Senior Data Scientist / AI-ML Engineer to lead the development and deployment of advanced machine learning solutions. The candidate will combine strong statistical and algorithmic expertise with practical production engineering skills to deliver scalable, reliable, and business-focused AI solutions. The role also includes mentoring data scientists and collaborating with cross-functional teams.

Key Responsibilities :

- Lead the design and development of end-to-end machine learning solutions, from problem formulation and experimentation to production deployment.

- Translate complex business challenges into analytical approaches, predictive models, and measurable outcomes.

- Develop advanced machine learning models for classification, regression, forecasting, recommendation, anomaly detection, and other relevant use cases.

- Apply feature engineering, feature selection, model optimization, and robust validation techniques.

- Evaluate and compare algorithms using suitable metrics, statistical methods, and business performance indicators.

- Design scalable training and inference pipelines for large and complex datasets.

- Develop deep learning solutions using frameworks such as PyTorch or TensorFlow where appropriate.

- Lead experimentation, model evaluation, error analysis, and performance improvement initiatives.

- Collaborate with data engineering and platform teams to build reliable data pipelines and production ML workflows.

- Implement MLOps practices covering experiment tracking, model versioning, automated testing, deployment, monitoring, and retraining.

- Monitor model performance, data drift, concept drift, and production reliability.

- Establish standards for reproducibility, model documentation, explainability, and responsible AI.

- Mentor junior data scientists and engineers, review technical work, and share best practices.

- Partner with product and business stakeholders to prioritize AI initiatives and communicate findings.

- Evaluate emerging ML techniques and frameworks to identify opportunities for improving existing solutions.

Required Skills :

- Advanced proficiency in Python and machine learning libraries such as scikit-learn, pandas, NumPy, PyTorch, or TensorFlow.

- Strong foundation in statistics, probability, optimization, experimental design, and mathematical modeling.

- Extensive experience with supervised learning, unsupervised learning, ensemble methods, and model optimization.

- Experience designing and evaluating deep learning or NLP solutions where relevant.

- Strong SQL skills and experience working with large, complex datasets.

- Experience with distributed data processing or big data technologies such as Spark is an advantage.

- Hands-on experience deploying and maintaining machine learning models in production.

- Knowledge of MLOps tools and workflows, such as MLflow, model registries, CI/CD, and automated model monitoring.

- Experience with cloud-based ML services and infrastructure on AWS, Azure, or GCP.

- Strong understanding of data quality, model interpretability, fairness, and responsible AI.

- Ability to guide technical decisions and mentor team members.

Preferred Qualifications :

- Experience with generative AI, large language models, NLP, computer vision, or recommendation systems, depending on project needs.

- Exposure to feature stores, model serving platforms, distributed training, or scalable inference systems.

- Experience building reusable ML components and standardized experimentation frameworks.

- Knowledge of data governance, privacy, and enterprise AI deployment requirements.

Key Competencies :

- Technical leadership, quantitative reasoning, model design, mentoring, stakeholder communication, and ownership of end-to-end machine learning delivery.