Data Scientist – Predictive Analytics / Predictive ML

Data Scientist – Predictive Analytics / Predictive ML

Location: Schaumburg, IL (Highly Preferred) or Remote for exceptional cases Employment Type: Contract | 3+ months | Strong possibility of extension Focus: Traditional Predictive Machine Learning / Structured & Tabular Data

Role Focus

• Hands-on Data Scientist focused on traditional predictive ML using tabular data
• Strong Python is essential
• Strong experience with tree-based models, feature engineering, data wrangling/data mining, and model evaluation
• Experience handling imbalanced / rare-event datasets is important, such as claims, fraud, credit risk, or similar use cases
• Insurance industry experience is required; P&C Insurance experience is preferred
• Data Engineering is not a core requirement; the focus is more on data preparation, quality, feature engineering, and predictive modeling
• Databricks preferred; equivalent experience with Snowflake, BigQuery, Fabric, Redshift, or Apache Spark is also acceptable

Position Summary

We are seeking a hands-on Data Scientist focused on traditional predictive machine learning using structured, tabular data. The role will develop, evaluate, and support productionization of predictive models for business problems such as risk, claims, fraud, retention, customer behaviour, and loss prediction.

The candidate must have professional experience in the Insurance industry, with P&C Insurance experience strongly preferred.

What the Client is Looking For

The ideal candidate has strong Python skills and hands-on experience with tree-based machine learning, feature engineering, data wrangling, data mining, model evaluation, and imbalanced/rare-event datasets.

Insurance industry experience is required. P&C Insurance experience is preferred, particularly experience involving claims, loss, fraud, risk, underwriting, pricing, retention, severity, or similar insurance analytics use cases.

Relevant predictive ML experience in areas such as claims, fraud, credit risk, customer risk, churn, or similar rare-event predictive use cases is highly relevant.

Key Responsibilities

• Develop, evaluate, tune, and support productionization of predictive ML models using structured/tabular data.
• Perform data wrangling, data mining, profiling, cleaning, EDA, and feature engineering.
• Build models using XGBoost, Gradient Boosting, Random Forest, and similar tree-based algorithms.
• Handle imbalanced and rare-event datasets such as claims, fraud, defaults, and risk events.
• Design appropriate training, validation, and testing strategies and identify data leakage or sampling issues.
• Apply appropriate evaluation metrics including Precision, Recall, ROC-AUC, PR-AUC, Lift, Ranking, and Calibration.
• Apply explainability techniques such as SHAP, feature importance, and partial dependence analysis.
• Support model testing, versioning, reproducibility, code reviews, and productionization.

Mandatory Requirements (Must Have)

• Demonstrated professional experience in the Insurance industry.
• Strong hands-on Python.
• Strong experience with predictive machine learning using structured/tabular data.
• Strong experience with tree-based algorithms such as XGBoost, Gradient Boosting, and Random Forest.
• Strong feature engineering, data wrangling, data mining, and EDA experience.
• Strong understanding of model evaluation, validation, and performance optimization.
• Experience with imbalanced datasets / rare-event prediction.
• Strong SQL skills.
• Experience with scikit-learn or similar ML frameworks.
• Ability to identify data quality issues and data leakage and develop appropriate validation strategies.
• Understanding of SHAP and model feature importance.

Preferred Requirements (Good to Have)

• P&C Insurance experience, particularly in:
• Claims
• Loss
• Fraud
• Risk
• Underwriting
• Pricing
• Retention
• Severity modeling
• Related P&C analytics
• Claims, fraud, loss, retention, customer lifetime value, risk, or severity modeling.
• Credit risk or other rare-event predictive modeling.
• Databricks experience.
• Snowflake, BigQuery, Microsoft Fabric, Redshift, Spark, or similar platforms.
• PySpark / distributed data processing.
• Git, MLflow, CI/CD, or MLOps.
• Experience taking predictive models into production.

Education & Certifications

• Bachelor's degree in Computer Science, Information Technology, or related discipline (or equivalent work experience).

Ideal Candidate

The ideal candidate is a hands-on traditional Data Scientist who spends most of their time working with structured/tabular data and predictive ML, rather than primarily GenAI or LLM technologies.

The candidate must have Insurance industry experience and should be particularly strong in Python, tree-based modeling, feature engineering, data mining, model evaluation, and rare-event/imbalanced prediction problems, with the ability to take models from data preparation through validation and productionization.

P&C Insurance experience is strongly preferred, especially experience with claims, loss, fraud, risk, underwriting, pricing, retention, or severity-related predictive analytics.

Important: GenAI, LLMs, NLP, embeddings, RAG, and vector search are nice-to-have exposure only—not the core of this role. The candidate should be a strong predictive ML Data Scientist first, with demonstrated Insurance industry experience.

Pay: From $50.00 per hour

Expected hours: 40.0 per week

Benefits:

• Dental insurance
• Health insurance
• Paid time off

Work Location: Remote

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