Design and implement robust ETL/ELT data pipelines for both structured and unstructured data
Build interactive dashboards and visualizations to communicate insights effectively
Develop, evaluate, and deploy machine learning and/or generative AI models for a variety of business use cases
Apply statistical analysis and mathematical modeling to derive insights from complex datasets
Collaborate with data engineers, analysts, software developers, and business teams to deliver data-driven solutions
Build and maintain scalable ML pipelines and APIs for real-time and batch inference
Ensure best practices in model versioning, reproducibility, observability, and governance (MLOps)
Stay current with trends in AI/ML, including LLMs and GenAI applications
Contribute to projects involving semantic search, knowledge graphs, or retrieval-augmented generation (RAG) as needed

Technical Skills
Proficiency in Python (e.g., Pandas, NumPy, FastAPI)
Strong experience with SQL and working with relational and NoSQL databases
Familiarity with big data technologies (e.g., Spark, Databricks, Delta Lake, Kafka)
Experience with one of the following cloud data platforms (e.g., Databricks, Microsoft Fabric, Amazon Redshift, Google BigQuery, Snowflake)
Experienced in report modeling and building dashboards with BI tools such as Power BI, Tableau, or Looker
Experience with one of the following ETL tools (Airflow, DBT, Azure Data Factory, AWS Glue, SSIS)
Solid knowledge and hands-on experience with building training/inferencing pipeline using machine learning algorithms such as classification, regression, clustering, time series forecasting, and anomaly detection using frameworks like Scikit-learn, PyTorch, and TensorFlow
Good foundation in machine learning concepts and deep learning (e.g., CNNs, RNNs, Transformers)
Math & Statistical Knowledge
Strong foundation in probability, statistics, optimization, and linear algebra
Experience in designing experiments (e.g., A/B testing) and interpreting statistical significance
Ability to apply math-driven thinking to AI/ML model design
Domain Knowledge
Experience with one of the following domains such as media, banking, finance, healthcare, retail, manufacturing, or insurance
Ability to understand and interpret domain-specific business problems and translate them into data science solutions
English Requirements
Good communication skills in English, both written and verbal
Soft Skills
Strong problem-solving and critical thinking abilities
Ability to clearly communicate technical findings to non-technical stakeholders
Collaborative, agile mindset with a self-starter attitude
Comfortable working in fast-paced, cross-functional teams
Nice to have
Experience with MLOps tools (e.g., MLflow, SageMaker, Azure ML, Vertex AI)
Awareness or working knowledge of LLMs (e.g., OpenAI models, Claude, LLaMA, Mistral)
Exposure to GenAI frameworks such as LangChain, Semantic Kernel, or Autogen
Understanding of prompt engineering, embeddings, and concepts like RAG, GraphRAG, Agentic AI
Familiarity with GenAI use cases such as building AI agents, chatbots, MCP, ...
Ability to integrate GenAI APIs (OpenAI, Azure OpenAI, Hugging Face) into applications