Data Scientist

Role overview

Key responsibilities

• Develop and deliver machine learning and deep learning models for a range of analytics use cases.
• Choose modeling techniques appropriate to product scope and constraints, business relevance, and technical robustness.
• Acquire, explore, and preprocess structured and unstructured data; engineer features, evaluate algorithms and architectures, and iteratively improve models.
• Identify valuable data sources and automate data collection and preparation where possible.
• Build, fine-tune, and maintain ML pipelines; integrate algorithms and packages into the platform and contribute models to its marketplace.
• Collaborate with engineering and DevOps on deployment and operationalization.
• Contribute to applied research and propose data-driven business solutions.
• Work with product, support, and client-facing teams to roll out AI solutions into trials and general availability.

Required skills and experience

• Strong theoretical and applied foundation in machine learning and deep learning; experience building pipelines for structured and unstructured data.
• Hands-on experience with TensorFlow, PyTorch, and machine learning libraries such as scikit-learn.
• Proficiency in Python and working knowledge of Java or R.
• Experience developing models exposed through REST APIs and containerized deployments using Docker.
• Applied NLP experience with transformer-based models and libraries such as Hugging Face, spaCy, or Gensim.
• Familiarity with SQL, Pandas, Apache Spark, and data processing at scale; experience with MLflow.
• Strong analytical thinking, clear communication, and ability to collaborate across multidisciplinary teams.

Desirable skills

• Large language model experience, including offline deployment scenarios, is desirable.
• Exposure to AWS, GCP, or Azure; data visualization tools; Jupyter Notebooks; or Git-based workflows is desirable.
• Education or finance dataset experience is an advantage.