Data Scientist

As a Data Scientist , you will apply machine learning and deep learning techniques to solve real-world business problems across numeric and text-based analytics. You will work across the full data science lifecycle-from data acquisition and feature engineering to model development and deployment-collaborating closely with engineering, product, and Dev Ops teams to deliver customer-centric AI solutions.

• Develop and deliver high-quality machine learning and deep learning models across a broad range of analytics use cases.
• Understand the practical scope and constraints of AI models within products and apply appropriate modeling techniques to ensure business relevance and technical robustness.
• Acquire data from diverse sources, performing data exploration and preprocessing on structured and unstructured datasets, conducting feature engineering, evaluating algorithms and architectures, and iteratively refining models to improve performance.
• Identify valuable data sources and automate data collection and preparation processes where possible.
• Build, fine-tune, and maintain machine learning pipelines, integrate algorithms and packages into the platform, and contribute new models to the platform marketplace.
• Collaborate closely with engineering and Dev Ops teams to support model deployment and operationalization.
• Contribute to applied research activities, propose data-driven solutions to business challenges, and work with product, support, and client-facing teams to ensure successful rollout of AI solutions into trials and general availability.

• Strong foundation in the theory and applied practice of machine learning and deep learning. Experience developing pipelines for structured and unstructured data.
• Hands-on experience with deep learning frameworks such as Tensor Flow and PyTorch, and machine learning libraries such as scikit-learn.
• Proficiency in Python, with working knowledge of Java or R.
• Experience developing and exposing models through RESTful APIs and containerized deployments using Docker.
• Strong coding practices, including appropriate use of data structures and clean, maintainable code.
• Applied experience in NLP, including the use of transformer-based models and libraries such as Hugging Face, spaCy, or Gensim. Familiarity with SQL, Pandas, Apache Spark, and data processing at scale.
• Experience using ML lifecycle tools such as MLflow.
• Strong analytical thinking, communication skills, and the ability to present technical concepts clearly to internal and external stakeholders.
• Ability to work collaboratively across multidisciplinary teams.

Experience working with large language models (LLMs), including offline deployment scenarios. Exposure to cloud-based environments (AWS, GCP, Azure). Experience with data visualization tools and collaborative environments such as Jupyter Notebooks and Git-based workflows. Domain experience with education or finance datasets is an advantage.