Responsible for the full lifecycle of analytical and machine learning model development, leading and supporting the team while contributing hands-on across all phases, from ideation and experimentation to implementation, testing, deployment, and optimization.
Responsibilities
- Lead and oversee the Data Science team across research, development, evaluation, fine-tuning, and deployment of ML and generative AI models, including statistical analysis, feature engineering, and hands-on contribution when needed
- Guide the design, implementation, and optimization of robust data pipelines and ML system architectures in collaboration with Data Engineering, ensuring scalability for both real-time and batch use cases
- Provide direction for creating analytical reports, dashboards, and model-driven visual insights to effectively communicate findings to business stakeholders
- Establish and enforce MLOps and technical quality standards - including model versioning, reproducibility, observability, governance, ensuring the team consistently follows these standards and providing guidance to help them apply the practices effectively
- Perform solution reviews, code reviews, and model validation to ensure consistent delivery quality
- Lead technical discussions with the team and clients, provide effort estimations, deliver technical training
- Support the PM with source-control and technical governance strategies
Qualifications
Technical Skills
- 5+ years of hands‐on experience in Data Science, ML, or Applied AI
- Proficiency in Python (e.g., Pandas, NumPy, FastAPI)
- Strong experience with SQL and working with relational and NoSQL databases
- Good knowledge of big data technologies (e.g., Spark, Databricks, Delta Lake, Kafka)
- Experience with at least one Cloud provider (e.g., Azure, AWS, or GCP)
- Experience using data visualization / BI tools such as Power BI, Tableau, or open-source BI tools
- Solid knowledge and hands-on experience with key machine learning algorithms such as classification, regression, clustering, time series forecasting, and anomaly detection using tools like Scikit-learn, PyTorch, and TensorFlow
- Strong 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 ETL tools (Airflow, DBT, Azure Data Factory, or AWS Glue)
- Experience with MLOps tools (e.g., MLflow, SageMaker, Azure ML, Vertex AI)
- Generative AI Skills
- Awareness or working knowledge of LLMs (e.g., GPT-4, Claude, LLaMA, Mistral)
- Exposure to GenAI frameworks such as LangChain, LlamaIndex, or Hugging Face Transformers
- Understanding of prompt engineering, embeddings, and concepts like RAG
- Familiarity with GenAI use cases such as summarization, document analysis, chatbots, or code generation
- Ability to integrate GenAI APIs (OpenAI, Azure OpenAI, Hugging Face) into applications