Data Scientist AI and ML

Area(s) of responsibility Design and develop robust AI/ML solutions tailored to industry verticals such as Banking, Financial Services, Insurance (BFSI), Energy & Utilities (EnU), Life Sciences, and Manufacturing.

Collaborate with cross-functional teams to gather business requirements and translate them into scalable machine learning architectures.

Lead the end-to-end lifecycle of ML projects—from data exploration and model development to deployment and monitoring—ensuring alignment with organizational standards and best practices.

Evaluate and select appropriate ML algorithms, frameworks, and tools to optimize model performance, scalability, and interpretability.

Conduct research on emerging ML techniques and technologies, providing insights for continuous innovation and improvement.

Define and implement data governance, model versioning, and deployment strategies to ensure compliance, reproducibility, and ethical use of AI.

Mentor junior developers and data scientists, fostering a collaborative and innovative team culture.

Communicate technical solutions and project progress to stakeholders and leadership, translating complex ML concepts into business-friendly language.

Must-Have Skills Bachelor’s or Master’s degree in Computer Science, Data Science, Artificial Intelligence, or a related field. 4+ years of experience in AI/ML development, with a strong focus on solution design, model development, and deployment.

Proven experience building ML solutions across multiple verticals, especially BFSI, EnU, Life Sciences, and Manufacturing.

Strong proficiency in programming languages such as Python or R, and hands-on experience with ML libraries like Scikit-learn, XGBoost, TensorFlow, or PyTorch.

Solid understanding of supervised, unsupervised, and reinforcement learning algorithms.

with cloud platforms (AWS, Azure, GCP) and their ML services (e.g., SageMaker, Azure ML, Vertex AI).

Strong analytical and problem-solving skills, with the ability to work in fast-paced environments.

Excellent communication skills to convey technical concepts to non-technical audiences.

Good-to-Have Skills Experience with big data technologies (e.g., Spark, Hadoop) and data engineering workflows.

Familiarity with MLOps practices and tools for CI/CD in ML pipelines (e.g., MLflow, Kubeflow, Airflow).

Exposure to AI governance frameworks and responsible AI practices.

Prior experience in consulting or client-facing roles delivering ML solutions.

Advanced certifications in AI/ML or cloud technologies (e.g., AWS Certified ML Specialist, Azure AI Engineer Associate).