Data Scientist

Data Scientist – Assistant Manager

Key Responsibilities

• Lead and own end-to-end data science and machine learning initiatives, from business problem identification and scoping through data preparation, modeling, validation, deployment, and monitoring.
• Develop predictive and prescriptive analytics solutions to support medical utilization management, including the detection of fraud, waste, abuse, and error (FWAE).
• Translate complex business and healthcare-related problems into data-driven analytical and machine learning solutions.
• Conduct exploratory data analysis (EDA) to identify trends, patterns, anomalies, relationships, and opportunities within healthcare and utilization data.
• Design, develop, test, and optimize machine learning models using appropriate algorithms and statistical techniques.
• Handle complex and highly imbalanced datasets, particularly rare-event problems such as FWAE and anomaly detection.
• Evaluate model performance using appropriate statistical and machine learning metrics, including Precision, Recall, F1-Score, ROC-AUC, RMSE, lift, and gain curves.
• Apply cost-sensitive evaluation and threshold optimization to assess the potential business value and deployment readiness of models.
• Perform data wrangling, cleansing, transformation, feature engineering, and validation to ensure data quality and model reliability.
• Develop analytical dashboards, visualizations, and reports to communicate insights and support management decision-making.
• Present analytical findings, model results, business implications, and recommendations to senior management and executive stakeholders.
• Translate technical and statistical findings into clear, actionable recommendations for Health Network Management, Utilization Management, and other business stakeholders.
• Identify opportunities to automate repetitive analytics and data science processes and implement scalable solutions.
• Collaborate with Data Analysts, Data Engineers, IT teams, healthcare teams, and business stakeholders to ensure successful implementation of analytics solutions.
• Establish and promote best practices in data science, model development, documentation, validation, and governance.
• Provide technical guidance and coaching to junior data scientists or analysts and contribute to the continuous improvement of the analytics team.
• Monitor deployed models and analytical solutions to ensure continued accuracy, relevance, and business effectiveness.
• Ensure that data science initiatives align with organizational objectives, healthcare business requirements, data privacy, and applicable policies and standards.

Required Experience

• 2–4 years of professional experience in Data Science, Machine Learning, Advanced Analytics, or a related field, with at least 1 year of hands-on data science experience.
• Experience developing and implementing end-to-end machine learning models in a professional or production environment.
• Experience in predictive and/or prescriptive analytics, including problem formulation, feature engineering, model development, evaluation, and interpretation.
• Experience using Python and SQL for data analysis, data manipulation, and machine learning.
• Experience with supervised and unsupervised machine learning techniques.
• Experience developing solutions involving anomaly detection, fraud detection, risk scoring, or similar use cases is an advantage.
• Experience in healthcare, HMO, insurance, claims, utilization management, fraud analytics, or financial risk analytics is highly preferred.
• Experience in process automation or developing reusable analytics pipelines is an advantage.
• Experience mentoring or providing technical guidance to junior analysts or data science professionals is preferred for the Assistant Manager level.
• Must have at least one end-to-end data science project that was personally owned and implemented for actual business use, beyond academic or coursework projects.

Qualifications

• Bachelor's  degree in Computer Science, Data Science, Statistics, Mathematics, Physics, Economics, Engineering, or another quantitative discipline.
• Strong proficiency in Python, particularly Pandas, NumPy, Scikit-learn, Seaborn, and Matplotlib.
• Strong proficiency in SQL, including JOINs, subqueries, CTEs, aggregations, and window functions.
• Strong knowledge of data cleaning, data wrangling, feature engineering, and data validation.
• Proficiency in handling data from different sources and formats, including CSV, JSON, SQL exports, and structured databases.