Data Scientist Regression Analysis
Role Summary
Analyze data to identify trends, detect variances, explain root causes, and support defect remediation
using statistical and regression analysis.
Key Responsibilities
Build and validate regression models to explain data drift and variances.
Analyze mismatches between expected and actual results.
Identify root causes of defects and data quality issues.
Perform statistical testing and trend analysis.
Create dashboards, heat maps, and visualizations to monitor drift.
Develop predictive models to detect issues early.
Partner with business, QA, and development teams to prioritize fixes.
Present findings and recommendations to stakeholders.
Required Skills
Strong knowledge of regression analysis and statistics.
Experience with Python, SQL, Big Data, Hadoop, and data visualization tools. Machine
learning experience is a plus.
Ability to analyze large datasets and identify patterns.
Experience with root cause analysis and anomaly detection.
Strong communication and problem-solving skills.
Preferred Experience
Financial services or payments experience.
Data reconciliation and validation.
Drift monitoring and predictive analytics.
Power BI, Tableau, Spark, Snowflake, or Databric
Success Measures
Faster defect diagnosis.
Earlier detection of drift.
Improved validation accuracy.
Reduced manual analysis effort.
Faster remediation and resolution of issues.
Ideal Candidate:
A data scientist who can use regression analysis, statistical modeling, and data visualization to detect
drift, explain variances, identify root causes, and help teams resolve defects faster.
Never miss an opportunity! Create an alert based on the job you applied for.
