Senior Data Scientist

We are seeking a talented and driven Senior Data Scientist to join our fast- growing startup focused on building innovative AI-powered products. This role involves working at the intersection of machine learning, data engineering, and product development, transforming complex data into impactful solutions.

You will play a key role in designing, developing, and deploying scalable AI models that directly influence product capabilities and business outcomes.

Key Responsibilities:

• Design, build, and deploy machine learning and AI models for real-world applications.

• Work with large, complex datasets to extract insights and drive decision-making.

• Develop and optimise data pipelines, feature engineering, and model training workflows.

• Collaborate with product managers, engineers, and stakeholders to translate business problems into data solutions.

• Evaluate model performance and continuously improve accuracy, scalability, and efficiency.

• Implement and maintain end-to-end ML lifecycle (ML Ops) practices

• Conduct exploratory data analysis (EDA) and communicate findings effectively.

• Stay updated with the latest advancements in AI, ML, and data science.

• Mentor junior team members and contribute to building a strong data science culture.

Required Skills & Qualifications:

• 6–8 years of experience in Data Science / Machine Learning roles.

• Strong proficiency in Python or R with hands-on experience in ML libraries (e.g., TensorFlow, PyTorch, Scikit-learn).

• Solid understanding of machine learning algorithms, statistics, and data modelling.

• Experience working with large datasets and distributed systems.

• Strong skills in data preprocessing, feature engineering, and model evaluation.

• Experience with SQL and data manipulation tools.

• Familiarity with cloud platforms (Azure, AWS, GCP).

• Ability to translate complex problems into scalable AI solutions.

• Strong analytical thinking and problem-solving skills.

• Strong knowledge of data ingestion, data pipelines, and large-scale data processing.

• Hands-on understanding of LLMs, Generative AI, RAG, and multi-agent AI systems.

• Experience working on AI-powered products involving structured and unstructured data.

• Exposure to agentic AI workflows, prompt engineering, vector databases, and model evaluation.

Good to Have:

• Experience with deep learning, NLP, computer vision, or generative AI.

• Knowledge of ML Ops tools and frameworks (e.g., MLflow, Kubeflow, Airflow).

• Experience deploying models using APIs, Docker, or Kubernetes.

• Exposure to real-time data processing and streaming systems.

• Prior experience working in a startup or fast-paced environment.

• Experience with Pharma or Life Sciences domain is an added advantage.