Data Scientist - AI/ML & Programmatic Optimization

Location: Mumbai Ciry, India; Portugal, Portugal

Department: AI & ML

Experience: 2-4 yrs

• Develop and implement machine learning models for marketplace yield optimization and bid/margin optimization.
• Build intelligent targeting recommendation systems that analyze user behavior and campaign data to suggest optimal audience segments, bid strategies, or placement opportunities - helping improve relevance, engagement, and performance.
• Design auto-optimization frameworks for programmatic campaigns using AI/ML (including reinforcement learning or advanced supervised approaches).

• Write production-quality Python code and build end-to-end ML pipelines (from data ingestion and feature engineering to training, evaluation, and deployment).
• Work with data engineers and infrastructure teams to operationalize models for real-time or batch serving.
• Ensure proper logging, monitoring, testing, and versioning of models in production.

• Conduct exploratory data analysis on large historical and real-time campaign and marketplace data to identify patterns, correlations, and business opportunities.
• Validate assumptions with data, design experiments (A/B or controlled studies), and generate actionable insights for product and operations teams.

• Partner with Product and Engineering to translate business problems into ML problem statements and solution designs.
• Communicate findings, model behavior, and performance implications clearly to technical and non-technical stakeholders.

• Define success metrics for models and optimization workflows; evaluate model performance rigorously and identify areas for improvement.
• Generate reports and dashboards to surface key trends and model impact to internal teams.

• 2-4 years of professional experience in data science, applied machine learning, analytics, or related fields.
• Strong programming skills in Python - capable of building reliable, maintainable, production-level code.
• Solid experience with SQL for large-scale data querying and transformation.
• Hands-on experience with machine learning libraries and frameworks (scikit-learn, TensorFlow, PyTorch, XGBoost, etc.).
• Experience in feature engineering, model training, evaluation, and deployment in production environments.

• Practical understanding of regression, classification, clustering, ranking, recommendation systems, or optimization algorithms.
• Experience with time-series models, reinforcement learning, or optimization frameworks is a strong advantage given the campaign and bid optimization focus.
• Ability to conduct statistical analysis, hypothesis testing, and extract meaningful insights from complex datasets.

• Comfortable translating analytics results and model behavior into business insights that drive decisions.
• Clear, effective communicator capable of explaining complex analysis and model details to stakeholders with varying technical backgrounds.

• Strong collaborator who thrives in cross-functional environments involving product, engineering, and operations.
• Data-driven, curious, and proactive - ready to explore new methods, push boundaries, and iterate rapidly on solutions.

• Experience with real-time data platforms (e.g., Kafka, Spark Streaming) and cloud services (AWS).
• Background in AdTech, programmatic auctions, DSP/SSP data analytics, or marketplace yield modeling.
• Familiarity with MLOps tools for ML deployment and monitoring (Airflow, Kubeflow, MLflow, etc.).