Data Scientist

Description:

What Youll Do:

- Build, deploy, and optimize predictive models across lending, collections, and CRM use cases.

- Work hands-on with large-scale datasets and modern data pipelines.

- Collaborate with cross-functional teams to translate business challenges into data-driven solutions.

- Apply best practices in MLOps, model monitoring, and governance.

- Communicate insights effectively to drive strategy and roadmap decisions.

- Oversee the end-to-end lifecycle of predictive model development, ensuring timely delivery and alignment with business priorities.

What Were Looking For:

- 4-7 years experience as a Data Scientist or ML Engineer, with proven experience productionizing ML models.

- Expertise in Python and libraries like scikit-learn, pandas, numpy, xgboost, PyTorch/TensorFlow, spaCy/NLTK.

- Strong SQL skills and comfort with cloud data lakes, ETL pipelines, and large messy datasets.

- Experience deploying models via REST APIs, Docker, batch/streaming workflows.

- Familiarity with data visualization / BI tools for business reporting.

- A practical MLOps mindsetversioning, monitoring, retraining, and governance.

- Excellent communicator who can operate independently and thrive in dynamic environments.

Nice-to-Have Skills:

- Experience with cloud ML platforms (SageMaker, Vertex AI, Azure ML, Databricks).

- NLP/NLU for chat or voice bots.

- Reinforcement learning or optimization exposure.

- Experience in regulated industries with focus on explainable AI.

- Familiarity with MLOps tools (MLflow, Kubeflow, Airflow, Dataiku).

- Contributions to open-source or research publications