Job Summary
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.
Responsibilities
- 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.
Qualifications
- 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.
About Cognizant:
Cognizant (Nasdaq: CTSH) is an AI Builder and technology services provider, bridging the gap between AI investment and enterprise value by building full-stack AI solutions for our clients. Our deep industry, process and engineering expertise enables us to build an organization’s unique context into technology systems that amplify human potential, drive tangible outcomes and keep global enterprises ahead in a fast-changing world. See how at cognizant.ai or @cognizant.
Additional employment information
Compensation information is accurate as of the date of this posting. Cognizant reserves the right to modify this information at any time, subject to applicable law.
Applicants may be required to attend interviews in person or by video conference. In addition, candidates may be required to present their current state or government issued ID during each interview.
Cognizant is an equal opportunity employer. Your application and candidacy will not be considered based on race, color, sex, religion, creed, sexual orientation, gender identity, national origin, disability, genetic information, pregnancy, veteran status or any other characteristic protected by federal, state or local laws.
If you have a disability that requires reasonable accommodation to search for a job opening or submit an application, please email [email protected] with your request and contact information.
