Data Scientist

Key Responsibilities

• Partner with clients and stakeholders to translate business challenges into analytical and AI-driven solutions.


• Perform exploratory data analysis (EDA) to identify trends, insights, and opportunities.


• Design, build, train, and evaluate machine learning, deep learning, and Generative AI models.


• Develop and optimize Retrieval-Augmented Generation (RAG) pipelines and LLM-based applications.


• Define model evaluation frameworks, conduct A/B testing, and monitor model performance and drift.


• Deploy data science solutions through APIs, batch pipelines, and cloud-based platforms.


• Optimize model inference performance, scalability, latency, and cost.


• Build accurate, explainable, and responsible AI solutions while minimizing bias and ensuring reliability.


• Collaborate with data engineers, software engineers, product managers, and designers to integrate solutions into production systems.


• Present insights and recommendations to technical and business stakeholders in a clear and actionable manner.


• Contribute to internal innovation initiatives, research projects, and thought leadership activities.

Required Qualifications

• Bachelor's, Master's, or PhD in Computer Science, Data Science, Machine Learning, Artificial Intelligence, Statistics, Mathematics, Engineering, or a related discipline.


• Strong programming skills in Python and  SQL.


• Hands-on experience with machine learning frameworks and the Python data science ecosystem.


• Experience developing predictive, classification, optimization, and statistical models using structured and unstructured data.


• Understanding of Generative AI, LLMs, prompt engineering, RAG architectures, and model evaluation techniques.


• Experience with model deployment, monitoring, testing, and productionization.


• Strong problem-solving and analytical thinking skills.


• Excellent communication and stakeholder management abilities.

Preferred Skills

• Experience with big data technologies such as PySpark, Hadoop, Hive, or similar frameworks.


• Familiarity with Databricks, Airflow, Kedro, Dask, RAPIDS, or comparable data engineering tools.


• Knowledge of cloud platforms including AWS, Azure, or GCP.


• Experience with containerization and orchestration tools such as  Docker and Kubernetes.


• Exposure to software engineering best practices and MLOps.


• Experience deploying AI and analytics solutions in enterprise environments.


• Consulting or client-facing project experience is highly desirable.