Associate Technical Architect - Data Scientist

Overview:

Azure Data Scientists

Key Responsibilities

● Translate high-level channel strategies into actionable data science use cases to optimize acquisition, retention, and efficiency.

● Partner with cross-functional leaders to define, monitor, and communicate KPIs for each distribution channel (e.g., sales funnel analytics, quote-to-policy ratios, CAC/LTV).

● Develop and deploy predictive models (lead scoring, next-best-action) to improve acquisition strategies.

● Create real-time dashboards for ongoing distribution performance monitoring.

● Work across multiple functions and use cases across insurance agency, distribution, digital experience and survey analytics.

● Analyze partner and channel performance to recommend resource allocation adjustments.

● Analyze complex insurance data (claims, policies, customer demographics, external data) to identify trends, patterns, and opportunities for business improvement and strategic decision-making.

● Research and implement cutting-edge data science techniques and tools to continuously improve analytical capabilities and explore new business opportunities.

● Design, build, and maintain robust, scalable, and production-ready data science solutions, ensuring their reliability and performance in live environments.

Qualifications

Industry & Domain Expertise

● 5+ years of dedicated analytics experience within the Life Insurance sector, demonstrating a deep understanding of insurance products, policy lifecycle, claims data, and regulatory considerations.

● Deep understanding of distribution models, agency performance dynamics, or digital product engagement.

● Proven track record in optimizing multi-channel strategies, agency networks, or digital journeys.

Stakeholder Management

● Experience engaging senior leaders and cross-functional teams (distribution, marketing, finance, UX, engineering).

● Ability to translate complex analytics into clear, actionable recommendations for non-technical stakeholders.

● Strong facilitation and presentation skills in customer-facing or consulting contexts.

Data Science Skills

● Expertise in statistical methods (regression, hypothesis testing, time series analysis, classification, clustering).

● Experience in uplift modeling, lifetime value modeling, personalization algorithms, or recommender systems.

● Strong grasp of causal inference, experimentation design, and dashboard development for self-service analytics.

Technical & Production Experience

● Proven experience designing, developing, and deploying machine learning models and data products into production systems, with a focus on scalability, monitoring, and maintenance.

● Hands-on proficiency with Databricks for data processing, model training, and deployment.

● Experience working with cloud platforms, specifically Microsoft Azure, including services relevant to data science such as Azure Data Lake Storage, Azure Synapse Analytics, and Azure Machine Learning.

● Proven ability to connect data insights to business strategies and identify opportunities for growth and optimization.

● Demonstrated success in delivering end-to-end data science solutions in a commercial setting.

● Experience leading analytics projects from requirement gathering through deployment and stakeholder adoption.

Nice-to-Have Skills

● Familiarity with other Azure data services (e.g., Azure Data Factory, Azure Functions) or Google Vertex AI, BigQuery, Adobe Analytics, or Google Analytics. (Adjusted for Azure emphasis)

● Experience in Generative AI and its application in distribution, agency, or digital experience optimization.

● Knowledge of insurance agent compensation models, training, and retention strategies.

● Experience with web behavioral analytics and deep learning for personalization.