Senior Data Scientist
About The Position
The Senior Data Scientist will use statistical expertise, machine learning, and data analysis to address complex business problems, support business decisions, and strengthen analytical capabilities. The role includes developing and deploying models, designing experiments, analyzing large datasets, building scalable data pipelines, and translating analytical findings into business recommendations. The position also designs AI agents and automated workflows, reporting pipelines, and interactive dashboards, while partnering with stakeholders and cross-functional teams to operationalize analytical solutions.
Requirements
- 12+ years of experience with PySpark and advanced SQL.
- 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, including Pandas, NumPy, SciPy, and 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.
- Ability to gather requirements from business stakeholders and communicate effectively with them.
- Ability to manage and deliver ad-hoc analytical requests under tight timelines.
Nice To Haves
- Experience in a fast-paced, agile environment.
- Familiarity with cloud platforms and big data technologies.
Responsibilities
- Apply supervised and unsupervised learning techniques, including regression, classification, clustering, and segmentation, to solve business problems.
- Design and analyze A/B tests using SQL and Python, define success metrics, and ensure statistically valid comparisons.
- Build and interpret predictive and diagnostic models to explain KPI changes and support what-if analysis.
- Monitor deployed models for drift, degradation, and data quality issues; retrain and recalibrate models as needed.
- Conduct hypothesis and significance testing and estimate confidence intervals.
- Perform exploratory analysis on high-volume transactional and event-based datasets to identify patterns, anomalies, and opportunities.
- Source, extract, clean, transform, and integrate structured and unstructured data from internal and external systems.
- Address missing data, outliers, and class imbalance using appropriate statistical techniques.
- Develop, debug, and optimize SQL and PySpark pipelines in Databricks for modeling, experimentation, and metric generation at scale.
- Validate source-of-truth data and lead root-cause analysis of data discrepancies.
- 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 support stakeholder self-service.
- Gather, document, and refine analytical requirements with stakeholders.
- Handle ad-hoc analytical requests with fast, accurate turnaround.
- Design and build interactive dashboards and visualizations in Qlik Sense and Plotly Dash.
- Translate model outputs into clear business recommendations with quantified expected impact.
- Partner with engineering, product, and operations teams to operationalize analytical solutions.
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What This Job Offers
Career Level
Senior
Education Level
No Education Listed
