Data Scientist Lead

We are seeking a highly skilled and versatile Data Scientist to join our growing data team. This role combines capabilities in data engineering, machine learning, generative AI, statistics, and domain understanding. You will be responsible for designing and implementing end-to-end AI/ML workflows — from raw data ingestion to deploying scalable models — while working closely with cross-functional teams to drive data-powered decisions and intelligence across our platforms.

Job descriptions

• 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

Qualifications

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