Job Summary
Serve as a senior data scientist within a hybrid work model designing and deploying robust analytics and machine learning solutions using Python Vertex AI BigQuery and GIT to address complex business challenges enhance decision quality and drive measurable value for global retail and merchandising focused initiatives across the enterprise.
Responsibilities
- Develop scalable machine learning models using Python to solve complex forecasting recommendation and optimization problems that directly improve business performance and customer satisfaction in a global context.
- Design and implement end to end data pipelines that move transform and validate large scale datasets in BigQuery ensuring accuracy consistency and timely availability of data for analytics and modeling activities.
- Configure and orchestrate production grade machine learning workflows on Vertex AI to enable reproducible experimentation model training tuning and automated deployment with strong focus on reliability and maintainability.
- Apply rigorous exploratory data analysis techniques to uncover hidden patterns anomalies and opportunities in structured and unstructured data that inform strategic decisions in merchandising and adjacent domains.
- Create robust version controlled repositories in GIT for code configuration and experiment artifacts enabling transparent collaboration traceable changes and safe rollback mechanisms for the entire data science lifecycle.
- Collaborate with product owners business analysts and engineering partners in a hybrid working environment to refine problem statements prioritize use cases and translate analytical findings into clear and actionable recommendations.
- Develop interpretable feature engineering strategies and data transformations that respect data quality constraints while maximizing predictive power model stability and fairness across diverse customer segments.
- Evaluate multiple algorithmic approaches using well defined metrics cross validation strategies and backtesting frameworks to select models that balance accuracy robustness latency and operational cost.
- Communicate complex model behavior assumptions and limitations through concise visualizations and narratives tailored to nontechnical partners to build trust and drive adoption of data driven solutions.
- Implement monitoring alerting and performance dashboards for models deployed through Vertex AI and BigQuery to detect data drift concept drift and service degradation before they impact business outcomes.
- Ensure compliance with data governance privacy and security guidelines by applying appropriate access controls anonymization techniques and documentation practices across all analytics assets.
- Mentor junior data science practitioners through code reviews knowledge sharing sessions and collaborative problem solving that raise the overall technical bar and promote a culture of responsible innovation.
- Align every analytics initiative with broader company objectives by quantifying business impact tracking realized value and highlighting how improved insights support more sustainable and inclusive outcomes for society.
Qualifications
- Possess ten to twelve years of hands on experience in advanced analytics and data science roles with a consistent record of delivering production grade machine learning solutions in complex organizational environments.
- Demonstrate expert proficiency in Python for data wrangling statistical analysis model development and automation including strong familiarity with widely used scientific and machine learning libraries.
- Show deep practical experience with BigQuery including writing optimized queries designing efficient schemas and managing large analytical workloads in a cloud native ecosystem.
- Exhibit strong working knowledge of Vertex AI services covering experiment tracking pipeline development model registry usage and deployment of scalable online or batch prediction services.
- Apply disciplined source control practices using GIT for branching code review and integration workflows that support collaborative development within distributed hybrid teams.
- Display solid understanding of merchandising or retail analytics concepts such as assortment optimization demand forecasting and promotion analysis even if exposure comes from partial or adjacent project experience.
- Demonstrate excellent communication and stakeholder engagement capabilities with the ability to translate technical insights into clear business language and influence decisions without relying on formal authority.
- Showcase strong problem solving mindset curiosity and adaptability with readiness to learn new tools techniques and domain knowledge to keep pace with evolving business and technology landscapes.
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.
