Senior Data Scientist

Serve as a senior data scientist responsible for designing and deploying scalable analytics and machine learning solutions using Databricks Azure Machine Learning and Python in a hybrid work model driving measurable value for global clients in retail customer services and utilities while advancing responsible data innovation that benefits society., * Develop advanced machine learning models using Python on Databricks to solve complex business problems and generate measurable value for clients in retail customer services and utilities domains.

• Design end to end data science workflows on Azure Machine Learning that cover data ingestion feature engineering model training evaluation and deployment in a production ready environment.
• Collaborate closely with business stakeholders to translate ambiguous analytical needs into clear data science use cases well defined hypotheses and measurable success criteria that align with organizational goals.
• Build scalable data pipelines on Databricks to process large and diverse datasets efficiently ensuring data quality consistency and timely availability for modeling and reporting activities.
• Perform comprehensive exploratory data analysis to uncover patterns detect anomalies and derive actionable insights that help improve customer experience operational efficiency and risk management.
• Implement robust model validation performance monitoring and drift detection practices to ensure that deployed models remain reliable fair and relevant over time in dynamic business environments.
• Document analytical approaches feature definitions model assumptions and experimentation outcomes in a clear and reusable manner to support transparency auditability and knowledge sharing across teams.
• Collaborate with data engineers analysts and product teams to integrate machine learning outputs into digital products reporting solutions and decision workflows that are easy to adopt for business users.
• Optimize model training and inference performance on Databricks and Azure Machine Learning by fine tuning algorithms managing compute resources effectively and applying efficient coding practices in Python.
• Apply domain understanding in retail customer services and utilities where available to frame relevant use cases such as demand prediction churn reduction pricing optimization and asset reliability improvement.
• Ensure responsible and compliant use of data by applying privacy aware design bias checks and appropriate anonymization techniques throughout the model development lifecycle.
• Mentor junior data professionals through guidance on coding standards model design choices documentation practices and experimentation strategies to uplift overall team capability.
• Engage with global client teams through hybrid working patterns to present findings explain model behavior in accessible language and recommend data driven actions that support strategic decision making.

• Demonstrate extensive experience in building and deploying machine learning solutions using Python with strong proficiency in libraries such as pandas scikit learn and relevant deep learning frameworks where applicable.
• Show proven hands on expertise in Databricks including notebook development cluster configuration optimization of Spark workloads and collaboration using version controlled environments.
• Exhibit practical experience with Azure Machine Learning including creation of workspaces pipelines experiments model registration and deployment of services that can integrate with broader enterprise platforms.
• Display strong understanding of data engineering concepts such as distributed processing data partitioning and performance tuning that enable reliable operation of large scale analytical pipelines.
• Possess solid grounding in statistics experimentation design and model evaluation techniques that enable rigorous comparison of approaches and trustworthy interpretation of results.
• Communicate complex analytical findings clearly to non technical audiences through structured storytelling effective visualization and context rich interpretation tailored to stakeholder needs.
• Bring useful exposure to retail customer services or utilities domains that supports problem framing selection of relevant metrics and design of solutions aligned with industry specific challenges.