Associate Technical Architect - Data Scientist

Quantiphi; Bengaluru; On-site; INR 1,500,000 - 2,000,000; Full time

Job summary

A leading data analytics company is seeking experienced Azure Data Scientists to optimize analytics strategies across various distribution channels. The role demands 5+ years of experience in the Life Insurance sector and expertise in data science, predictive modeling, and engaging with senior leadership. Successful candidates will translate strategic objectives into actionable insights, contributing to customer acquisition and retention initiatives. This is an opportunity to leverage advanced analytics solutions to drive measurable business impact.

Qualifications

• 5+ years of analytics experience in the Life Insurance sector.
• Experience in optimizing multi-channel strategies.
• Ability to engage senior stakeholders effectively.

Responsibilities

• Translate high-level strategies into actionable data science use cases.
• Develop predictive models to improve acquisition strategies.
• Research and implement cutting-edge data science techniques.

Skills

Statistical methods

Experience with Databricks

Causal inference

Machine learning models

Azure Data Lake Storage

Uplift modeling

Strong presentation skills

Tools

Azure Synapse Analytics

Azure Machine Learning

Google Analytics

Job description

We are seeking experienced Azure Data Scientists to lead analytics across our multi-channel distribution, exclusive and independent agency network, and digital customer experience. These roles bridge strategic business goals and actionable data science initiatives, driving measurable impact on customer acquisition, retention, engagement, and operational efficiency. Successful candidates will partner with senior leaders across distribution, marketing, product, UX, and technology teams to design and implement advanced analytics solutions that optimize performance and create value across the customer and agent journey.

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.
• 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.