Data Scientist
Job Overview
We are looking for an experienced Data Scientist to develop and manage end-to-end machine learning and analytics solutions within a production environment.
The candidate will work with established production data layers to build, test, train, deploy, and maintain machine learning models and pipelines. The role involves working on traditional machine learning, advanced analytics, and deep learning use cases based on business requirements.
In addition to model development, the candidate will create Power BI dashboards and analytical reports, translate technical results into actionable business insights, and manage stakeholder expectations regarding model performance, reporting capabilities, data quality, and delivery timelines.
Key Responsibilities
- Design, develop, test, train, validate, deploy, and maintain machine learning and deep learning models.
- Build solutions for use cases such as classification, regression, clustering, forecasting, customer segmentation, churn prediction, recommendation systems, customer lifetime value, and anomaly detection.
- Build and maintain end-to-end machine learning pipelines covering data processing, model training, validation, registration, deployment, monitoring, and retraining.
- Use MLflow for experiment tracking, model versioning, model registration, and lifecycle management.
- Deploy batch or real-time machine learning solutions on Azure.
- Monitor production models for data drift, performance degradation, pipeline failures, and changes in upstream data.
- Develop scalable data-processing workflows using Python, SQL, and PySpark.
- Develop and maintain interactive Power BI dashboards, reports, KPIs, and stakeholder-facing analytical outputs.
- Conduct ad-hoc analysis and clearly communicate insights, recommendations, risks, and model limitations to stakeholders.
- Translate business requirements into technically feasible machine learning, analytics, and reporting solutions.
- Manage stakeholder expectations regarding data availability, model accuracy, reporting capabilities, timelines, and production readiness.
- Maintain appropriate documentation for data flows, models, pipelines, assumptions, dependencies, deployment processes, and reporting logic.
- Collaborate with data engineering, analytics, reporting, technology, and business teams in a client-facing environment.Technical Skills
- Experience: 5+ Years.
- Strong hands-on experience with Python, SQL, and PySpark
- Strong knowledge of traditional and advanced machine learning techniques
- Experience with deep learning frameworks such as PyTorch or TensorFlow
- Hands-on experience with Microsoft Azure and Azure Databricks
- Experience with MLflow for experiment tracking, model registration, and model lifecycle management
- Strong experience developing dashboards and reports using Power BI
- Experience building and maintaining end-to-end MLOps pipelines
- Experience deploying and monitoring machine learning models in production familiarity with Delta Lake, Databricks Workflows, Unity Catalog, Azure Data Lake Storage, Azure Data Factory, Azure Machine Learning, and Azure DevOps
- Excellent communication skills, with the ability to work independently in a client-facing environment