Data Scientist

Role Overview

As a Junior Data Scientist in the Data & AI Delivery team, you will work closely with senior data scientists and delivery leads to design, develop, and deploy AI/ML solutions that address real-world business problems. This role is ideal for candidates who are passionate about data science, eager to learn cutting-edge AI technologies, and ready to grow into a future technical leader.

Key Responsibilities

Solution Development & Implementation

• Assist in building end-to-end data science workflows including data ingestion, preprocessing, modeling, and deployment.
• Assist in deploying models into applications via

  APIs

  for real-world use cases
• Develop, test, and validate machine learning models under guidance from senior team members.
• Support integration of AI/ML models into business applications and existing platforms.

Data Manipulation & Analysis

• Perform exploratory data analysis (EDA), feature engineering, and data cleaning using Python, SQL
• Work with big data processing frameworks such as Spark/Hadoop in real project settings.

Generative AI & Advanced ML (Exposure & Learning)

• Gain exposure to Generative AI (e.g., GPT, Hugging Face Transformers) and contribute to fine-tuning and experimentation tasks.
• Participate in pilots involving GenAI for text generation, conversational AI, and content automation.

MLOps & Deployment Support

• Learn and assist in deploying ML models using MLOps pipelines and tools (MLflow, TFX, Kubeflow).
• Help monitor model performance, track metrics, and troubleshoot issues in production.
• Possess hands-on exposure to

  Kubernetes

  for orchestrating and managing AI workloads.

Collaboration & Documentation

• Work alongside senior data scientists, engineers, and business stakeholders to understand requirements.
• Document workflows, experiments, and project outcomes for knowledge sharing and reusability.

Required Skills & Qualifications

• Strong programming foundation in Python (R/Scala is a plus).
• Good understanding of machine learning concepts and libraries (scikit-learn, TensorFlow, PyTorch).
• Hands-on experience with SQL and data wrangling.
• Familiarity with APIs (REST/GraphQL) for integrating ML models.
• Basic understanding or exposure to Kubernetes and Docker.
• Exposure to cloud platforms (AWS, Azure, or GCP) in projects/internships.
• Curiosity and eagerness to learn advanced areas such as MLOps, Big Data, and Generative AI.

Preferred Background

• Bachelors/Master’s in Computer Science, Data Science, Statistics, or related field.
• Strong experience in building genAI applications
• Internship, project work, or Kaggle/competition experience in ML/AI.
• Strong analytical and problem-solving skills with a collaborative mindset.

What We Offer

• Mentorship from senior Data & AI leaders.
• Hands-on experience with APIs, Kubernetes, and cloud-native AI deployments.
• Exposure to enterprise-grade ML/AI and Generative AI solutions.
• Platforms to contribute to thought leadership through knowledge sharing, innovation showcases, and community engagement.
• A clear learning path towards Data Scientist / MLOps Engineer / AI Specialist roles.

  Role & responsibilities

Preferred candidate profile