Data Analytics Lead with procurement domain

Role : Data Analytics Lead with procurement domain

Client Full Address: Mountain View CA (Onsite)

Hire Type (FTE/Contract): Contract

No. of client interviews for this role: 2

Rate: $68

Required Skills

- Strong foundation in statistics, probability, and experimental design

- Experience developing and deploying statistical and machine learning models end to end

- Advanced SQL skills and strong Python proficiency (Pandas, NumPy, SciPy, scikit-learn)

- Hands-on experience with Databricks — SQL, PySpark, and pipeline development

- Experience building AI agents or automations using Claude or comparable AI/LLM tools

- Proficiency with data visualization and BI tools, including Qlik Sense and Plotly Dash

- Demonstrated ability to gather requirements from and communicate effectively with business stakeholders

- Proven ability to manage and deliver ad-hoc analytical requests under tight timelines

Apply supervised and unsupervised learning techniques — including regression, classification, clustering, and segmentation — to solve business problems

- Design and analyze controlled experiments (A/B tests) using SQL and Python, defining success metrics, ensuring statistically valid comparisons between control and test groups, and translating results into actionable recommendations

- Build and interpret predictive and diagnostic models to explain drivers of KPI movement, quantify expected outcomes under different business scenarios, and support what-if analysis for leadership

- Monitor deployed models for drift, degradation, and data quality issues, retraining and recalibrating as needed

- Conduct statistical hypothesis testing, significance testing, and confidence interval estimation to validate analytical conclusions

- Conduct exploratory data analysis on high-volume transactional and event-based datasets to identify patterns, anomalies, distributions, and opportunities

- Source, extract, clean, transform, and integrate structured and unstructured data from multiple internal and external systems

- Handle missing data, outliers, and class imbalance using appropriate statistical techniques to minimize bias and improve model reliability

- Develop, debug, and optimize SQL and PySpark pipelines in Databricks to support modeling, experimentation, and metric generation at scale

- Perform source-of-truth data validation and lead root cause analysis on data discrepancies, resolving issues related to caching, date filtering, and source data gaps

- Design, build, and deploy AI agents using Claude or comparable AI/LLM tools to automate recurring analytical and reporting workflows

- Build automated reporting pipelines and contextual knowledge layers that distribute key metrics and enable stakeholders to self-serve insights

- Work directly with stakeholders to gather, document, and refine analytical requirements, translating business questions into clear methodological specifications

- Take on ad-hoc analytical requests and deliver them with fast, accurate turnaround, prioritizing them alongside roadmap work

- Design and build interactive dashboards and analytical visualizations in Qlik Sense and Plotly Dash to communicate model outputs, experiment results, and KPI performance

- Translate model outputs into clear business recommendations with quantified expected impact

- Partner with engineering, product, and operations teams to operationalize analytical solutions and integrate them into business workflows