Data Scientist

Job Purpose

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 DevOps teams to deliver customer-centric AI solutions.

Job Description

Key Responsibilities

• 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 DevOps 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.

Key Skills

• 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 TensorFlow 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.

Qualifications and Experience

Preferred Educational Qualifications and Professional Certifications

Bachelor's degree in Artificial Intelligence, Data Science, Business Analytics, Computer Science, Mathematics, Engineering, or a related field is preferred. Relevant professional certifications are an advantage.

Experience

Minimum 4–6 years of professional experience in data science or applied machine learning roles.

Desirable Skills

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.