Data Scientist

Data Scientist Pleasanton, CA Job Details Contract 3 hours ago Qualifications Data model design Azure Data transformation pipeline development Continuous Delivery (CD) implementation Hypothesis testing Data modeling projects Spark NumPy Application deployment Google Cloud Platform Scalable systems Improving operational efficiency Automating deployment processes SQL Pandas Data Architecture Design (Architecture design skills) AWS Spark implementation Model deployment Decision making Statistical modeling Mentoring Natural language processing Experimental design Scalability Regression analysis Predictive analytics A/B testing Senior level AI Cross-functional collaboration Machine learning frameworks Data-driven decision making Project stakeholder communication MLOps Generative AI Cross-functional communication Hadoop Full Job Description Job Summary We are seeking a dynamic and innovative Data Scientist to join our analytics team and drive data-driven decision-making across the organization. In this role, you will harness the power of big data, advanced machine learning techniques, and cutting-edge AI frameworks to uncover insights, develop predictive models, and optimize business processes. Your expertise will help shape strategic initiatives, enhance operational efficiency, and deliver actionable intelligence that propels our organization forward. This position offers an exciting opportunity to work with diverse datasets, collaborate with cross-functional teams, and deploy scalable solutions in a fast-paced environment.

Advanced Data Engineering & Architecture:

Architects scalable data pipelines and ETL processes using Python(Pandas, NumPy) , SQL, and Cloud technologies (AWS/GCP/Azure). Capable of designing data models for both structured and unstructured data at scale.

Machine Learning Leadership:

Leads the end-to-end ML lifecycle, selecting appropriate algorithms for complex business problems. Proven track record of deploying high-impact models (Regression, Classification, Deep Learning, or NLP) that drive measurable business value. Should have working idea of GenAI and RAG architecture.

Statistical Strategy & Experimentation:

Designs and oversees complex A/B tests and causal inference studies using probability, hypothesis testing, statistical modeling and exploratory data analysis (EDA). Advises stakeholders on statistical validity and interprets data insights to guide strategic business decisions.

MLOps & Production Excellence:

Owns model reliability in production. Implements robust MLOps practices (CI/CD, model monitoring, drift detection) and collaborates with Engineering teams to ensure system scalability and latency requirements are met. Must have hands-on knowledge of production deployment and finetuning

Big Data & Data Engineering Knowledge:

Experience working with large-scale data processing frameworks such as Spark and Hadoop, and building efficient data pipelines.

Mentorship & Collaboration :

Mentors junior data scientists, conducts code reviews, and fosters a culture of best practices. Effectively communicates technical concepts to non-technical stakeholders and executive leadership.

Work Location:

In person