Senior Data Scientist

We're hiring a Senior Data Scientist to lead the design, development, and deployment of advanced machine learning and AI-driven solutions across AWS and GCP. You'll own end-to-end model lifecycle - from data exploration to production - and collaborate closely with data engineering teams to ensure
scalability and reliability.
This role also involves mentoring data scientists, driving MLOps best practices, and supporting presales and proposals through solution design, estimation, and technical presentations to customers. Experience with AIOps/MLOps, data observability, and AI platform optimization is a strong plus
Modeling & Solution Design
Develop, train, and optimize machine learning and statistical models for predictive analytics, recommendation systems, forecasting, and anomaly detection.
Translate business problems into analytical solutions and communicate insights to non-technical stakeholders.
Design robust feature engineering, data preprocessing, and model validation frameworks.
Select appropriate ML algorithms and technologies for scalability, interpretability, and cost efficiency.
AI Platform & MLOps
Collaborate with Data Engineering teams to operationalize models in production environments.
Implement MLOps pipelines for model versioning, CI/CD, monitoring, and retraining (using tools such as SageMaker, Vertex AI, or MLflow).
Define and track model performance metrics, data drift, and retraining triggers.
Integrate observability and AIOps tools for proactive issue detection and system resilience.
Team Leadership
Mentor junior data scientists and analysts through code reviews, modeling best practices, and project guidance.
Drive model governance, documentation, and experimentation culture across the team.
Partner with engineering and business leaders to define data science strategy and delivery roadmap.
Customer & Presales Engagement
Work closely with business and sales teams to understand customer challenges and translate them into AI/ML solutions.
Support proposal creation, including architecture options, effort estimation, and value articulation.
Deliver presentations, demos, and technical discussions to help clients understand model capabilities and outcomes.
Research & Innovation
Explore and evaluate emerging trends in AI/ML, generative AI, and LLMs to guide platform evolution.
Prototype and benchmark new models or frameworks to improve performance, cost, and explainability.
Contribute to reusable assets, accelerators, and internal knowledge sharing.