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Manulife · 1 day ago

Associate Data Scientist - Fraud Analytics

Boston, MA

Full-time

Hybrid

Entry Level

$81K/yr - $134K/yr

2+ years exp

Manulife is a leading international financial services provider, and they are seeking a highly analytical and creative Associate Data Scientist to join their advanced analytics team. This role focuses on fraud detection and digital risk mitigation, offering the opportunity to develop sophisticated models and innovative solutions to protect the organization and policyholders from fraudulent activities while ensuring efficient processing of legitimate claims.

FinanceFinancial ExchangesFinancial Services

Responsibilities

Design and build sophisticated fraud detection models with emphasis on time series analysis to identify temporal patterns and trends in fraudulent behavior

Develop anomaly detection systems to flag unusual claims patterns, provider behaviors, and policyholder activities

Create graph-based models to uncover fraud rings, provider networks, and suspicious relationship patterns

Build ensemble models that combine temporal, network, and statistical approaches for comprehensive fraud detection

Perform advanced statistical analysis on large, complex datasets to uncover fraud indicators

Leverage large language models (LLMs) for analyzing unstructured claims data, policy documents, and investigator notes to identify fraud indicators

Design and implement digital controls and automated workflows to mitigate fraud impact

Develop innovative analytical solutions to address emerging fraud schemes and attack vectors

Create data-driven business rules and decision frameworks for fraud prevention

Build monitoring systems and dashboards to track model performance and fraud trends

Deploy and monitor machine learning models in production environments using MLOps best practices

Implement model versioning, A/B testing, and continuous integration/deployment pipelines for fraud detection systems

Design real-time model serving infrastructure for low-latency fraud scoring

Establish model performance monitoring, drift detection, and automated retraining workflows

Collaborate with engineering teams on scalable AI system architecture and deployment strategies

Qualification

PythonSQLAI techniquesGraph AnalyticsMLOpsCloud environmentsData analytics life cycleMachine learning life cycleCommunication skillsProblem-solving

Required

Master's degree or PhD degree in quantitative fields such as Statistics, Applied Mathematics, Data Science, Engineering, or Computer Science or Physics

Proficient in programming using Python and SQL

At least 2-year of industry experience in developing and deploying models using AI and GenAI techniques

Experience in using Python (e.g., Pandas, NLTK, Scikit-learn, Keras etc.), common LLM development frameworks (e.g., Langchain, Semantic Kernel), Relational storage (SQL), Non-relational storage (NoSQL)

Preferred

Experience with fraud detection, risk analytics, or financial crime prevention preferred

Advanced Graph Analytics: Experience implementing graph-based fraud rings detection, money laundering networks, and provider relationship analysis

Proven problem-solving abilities, including conducting root cause analysis to address specific business inquiries and identify opportunities for enhancement

Excellent communication skills to explain complex topics to diverse audiences

Demonstrated expertise in the data analytics life cycle, encompassing problem framing, data collection, data cleansing, insights generation, reporting, and communication

Skilled in machine learning modeling life cycle, including exploratory data analysis, data cleansing, feature engineering, model building, deployment, and monitoring

Experience in developing and deploying models in cloud-based environments, specifically Microsoft Azure, and Databricks, following MLOps best practices

Experience with Git Version Control, Unit/Integration/End-to-End Testing, CI/CD, release management, etc

Benefits

Health, dental, mental health, vision, short- and long-term disability, life and AD&D insurance coverage

Adoption/surrogacy and wellness benefits

Employee/family assistance plans

Various retirement savings plans (including pension/401(k) savings plans and a global share ownership plan with employer matching contributions)

Financial education and counseling resources

Generous paid time off program in the U.S. includes up to 11 paid holidays, 3 personal days, 150 hours of vacation, and 40 hours of sick time (or more where required by law) each year

Full range of statutory leaves of absence