Urgent Req: Data Scientist Santa Clara, CA ( Local ) Onsite, CTC

[Murali Krishna Tummala]

No Fake GCs

Urgent Req: Data Scientist  Santa Clara, CA ( Local ) Onsite, CTC

Onsite, Santa Clara, CA

Data Scientist

Need genuine experience and genuine visa and Linked in  

Mail me Resume kri...@readpointe.com

Primary Responsibilities  
1. Product Analytics Leadership  
Partner with Product Managers, Engineers, Data Scientists, and UX teams to define success metrics, measurement strategies, and opportunity assessments for product feature enhancements and recommendation algorithms.  
Translate ambiguous business or product questions into structured analytical approaches that influence prioritization and product direction.  
 
2. Deep Customer & Behavioral Analysis  
Work closely with engineering and product to build clickstream tracking 
Leverage clickstream data, user logs, and customer engagement patterns to understand how customers interact with site features.  
Partner with personalization data science to increase adoption of personalization algorithms across discovery pages 
Conduct funnel analysis, opportunity sizing, cohort analysis, attribution, and retention of Walmart customers.  
 
3. Experimentation Strategy & Execution  
Design and evaluate large-scale A/B tests and advanced experimentation methods (e.g., quasi-experimental approaches, causal inference).  
Ensure experiment integrity through appropriate power analysis, guardrail metrics, and thoughtful statistical methodologies.  
Produce end-to-end experiment readouts with clear conclusions, implications, and directional recommendations grounded in customer behavior.  
 
4. ML-Aware & Technical Execution  
Use expert-level SQL to analyze large-scale datasets across platforms such as BigQuery, Hive, and Spark to uncover user behavior insights to use for model building and ad-hoc analysis 
Develop reproducible analytical pipelines and experiment workflows using Python and PySpark.  
Familiarity with ML concepts (e.g., predictive modeling using supervised and unsupervised ML methods, dimensionality reduction, clustering). 
Apply data mining and feature-level diagnostics to identify patterns that impact customer experience or ML performance.  
 
5. AI aware  
Use prompt engineering and LLMs to simplify day-to-day work 
Hands-on experience to create innovative tools for workflow automation 
 
6. Storytelling & Insight Influence  
Deliver compelling, concise narratives that translate analytical findings into product and business implications.  
Influence product and engineering leaders through data-backed recommendations and clear articulation of trade-offs.  
 
7. Cross-Functional Collaboration  
Build strong relationships with Product, Engineering, Business and Data Science partners.  
Serve as a trusted voice who anticipates stakeholder needs, clarifies expectations, facilitates alignment, and drives shared accountability.