Senior Data Scientist

Role Description

We are looking for a Senior Data Scientist to drive predictive analytics and machine learning initiatives for enterprise use cases. This role focuses on building scalable, production-ready ML solutions using traditional machine learning techniques across domains such as forecasting, risk modeling, and customer analytics.

The ideal candidate will be a hands-on leader who can translate business problems into data-driven solutions, define modeling strategies, and lead end-to-end implementation on AWS while ensuring measurable business impact.

• Lead end-to-end data science initiatives for predictive analytics use cases such as demand forecasting, churn prediction, and risk modeling.
• Translate business requirements into ML problem statements and define appropriate modeling approaches.
• Design, build, and deploy machine learning models using traditional ML techniques (regression, classification, clustering, time series).
• Drive feature engineering, data preparation, and exploratory data analysis to improve model performance.
• Develop and manage scalable ML pipelines from data ingestion to model deployment.
• Deploy and manage models on AWS using services such as SageMaker.
• Ensure model performance through validation, monitoring, and periodic retraining.
• Collaborate with data engineering and MLOps teams to productionize ML solutions.
• Apply best practices for model governance, explainability, and responsible AI.
• Mentor junior data scientists and provide technical leadership while remaining hands-on.
• Communicate insights, model outputs, and recommendations effectively to business stakeholders.

Qualifications

• 8+ years of relevant hands-on technical experience implementing and developing cloud solutions on AWS.
• Strong experience leading predictive analytics initiatives using traditional ML techniques including regression, classification, clustering, and time series forecasting.
• Hands-on experience with time series forecasting models including SARIMA, Prophet, and other ML-based forecasting approaches.
• Proficiency in Python with experience in libraries such as scikit-learn, XGBoost, Pandas, NumPy.
• Knowledge of a variety of machine learning techniques (Supervised/unsupervised etc.) (clustering, decision tree learning, artificial neural networks, etc.) and their real-world advantages/drawbacks.
• Proven ability to translate complex business problems into scalable ML solutions, driving feature engineering strategies and end-to-end model development.
• Hands-on experience on AWS Machine Learning services.
• Proven experience using AWS Sagemaker leveraging different types of data sources, Training jobs, real-time and batch Inference, and Processing Jobs.
• Experience leading model deployment on AWS SageMaker with a strong focus on performance optimization, model governance, and measurable business impact.
• Implement and manage MLOps based model lifecycle and best practices for ML architecture in production environments.
• Experience with at least one of the workflow orchestration tools, Airflow, StepFunctions, SageMaker Pipelines, Kubeflow etc.
• Ability to create end-to-end solution architecture for model training, deployment, and retraining using native AWS services such as Sagemaker, Lambda functions, etc.
• Experience in building model monitoring and explainability workflows in production environments.

Requirements

• Experience defining and driving model governance frameworks and performance monitoring strategies in production environments.
• Ability to collaborate with cross-functional teams such as Developers, QA, Project Managers, and other stakeholders to understand their requirements and implement solutions.
• Experience with Generative AI development.
• Experience working on Infrastructure as Code (IaC) and CI/CD pipelines.

Benefits

• Join one of the world’s fastest-growing AI-first digital engineering companies and make a real impact at scale.
• Lead and collaborate with a high-energy team of talented, driven individuals solving complex, meaningful challenges.
• Work with Fortune 500 companies and disruptive innovators in a research-driven environment with 60+ patents.
• Stay ahead of the curve by gaining hands-on experience with cutting-edge AI, ML, data, and cloud technologies while continuously upskilling.
• If you like wild growth and working with happy, enthusiastic over-achievers, you'll enjoy your career with us!