the Role
We are looking for an experienced Data Scientist to design, build, and deploy scalable machine learning solutions across key business domains such as lending, collections, and customer engagement (CRM). You will work hands-on with large datasets, modern data pipelines, and cloud-based infrastructure to deliver models that drive measurable business impact.
What Youll Do
- Build, deploy, and optimize predictive and machine learning models for lending, collections, and CRM use cases.
- Work directly with large-scale datasets and modern data pipelines to ensure data quality and accessibility.
- Partner with cross-functional teams to identify opportunities and translate business challenges into data-driven solutions.
- Implement best practices in MLOps, including model monitoring, retraining, and governance.
- Develop automated workflows for model deployment (REST APIs, batch / streaming jobs, Docker).
- Communicate actionable insights and recommendations to influence product strategy and decision-making.
What Were Looking For
47 years of experience as a Data Scientist, with a proven track record of productionizing ML models.Strong programming skills in Python and expertise with key libraries :scikit-learn, pandas, numpy, xgboost, PyTorch / TensorFlow, spaCy / NLTK.
Proficiency in SQL and experience working with cloud data lakes, ETL pipelines, and large, unstructured datasets.Experience deploying models through REST APIs, Docker, and batch or streaming workflows.Familiarity with data visualization and BI tools for reporting and insight generation.A practical MLOps mindset, including model versioning, monitoring, retraining, and governance.Excellent communication skills and the ability to thrive in fast-paced, dynamic environments.Nice-to-Have Skills
Experience with cloud ML platforms such as AWS SageMaker, Google Vertex AI, Azure ML, or Databricks.Exposure to NLP / NLU techniques for chat or voice-based systems.Familiarity with reinforcement learning or optimization algorithms.Experience in regulated industries with a focus on explainable AI and model interpretability.Hands-on with MLOps tools like MLflow, Kubeflow, Airflow, or Dataiku.Contributions to open-source projects or research publications.(ref : iimjobs.com)