IT AVP – Data Science

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The AVP of Data Science is a pivotal role within the Data Products domain. This role will be responsible for managing and developing teams, building organizational capability, and setting and communicating direction. It will identify opportunities to deploy extensive knowledge of data science techniques, challenging the status quo, and designing and implementing scalable data science outputs and products. The role will drive collaboration with group companies and departments with IT to support our organizational objectives, select the most appropriate tools and technologies, navigate ethical and data challenges, and effectively communicate and present findings.

The AVP of Data Science will provide expert advice to evaluate problems that need data science solutions and are proficient in handling large and complex data sets, including structured, unstructured, and semi-structureddata.

In addition to technical expertise, the AVP of Data Science will play a crucial role in team dynamics and professional development. They will be responsible for managing the Data Science team, planning projects, and building analytical models by leveraging large volumes of data from various sources

The role also involves leading the development of analytics-driven products using machine learning, natural language processing, and mathematical techniques to develop powerful science in collaboration with the Emerging Tech. leader.

Furthermore, the AVP of Data Science will ensure the integration of AI, predictive analytics, and automation into core business functions, such as underwriting, pricing, and claims processing, while staying ahead of industry trends, regulatory changes, and technological advancements

Detailed Job Description

• Predictive Modeling:
  Develop predictive models to enhance risk assessment, fraud detection, and customer segmentation strategies.
• Claims Analysis:
  Analyze claims data to identify patterns, improve processing efficiency, and detect fraudulent activity.
• Pricing Optimization:
  Work closely with Actuarial teams to optimize pricing models and assess market competitiveness.
• Risk Assessment:
  Build advanced models for risk prediction and mitigation, leveraging structured and unstructured data.
• Data Preparation:
  Clean, preprocess, and integrate data from multiple sources, including internal systems and external third-party datasets.
• Oversee the development of the Medalion data architecture for TMNAS and its Group Companies
• Customer Insights:
  Identify trends in customer behavior to improve retention, tailor policy offerings, and enhance the overall customer experience.
• Collaboration:
  
  Partner with Underwriting, Actuarial, and Claims teams to align data science initiatives with business objectives.
• Visualization:
  Oversee the creation insights and reports to present insights to non-technical stakeholders, supporting data-driven decisions.
• Technical
  
  Skills:
• Proficient in Python, R, or similar programming languages.
• Experience with SQL and data manipulation in databases.
• Familiarity with machine learning frameworks (e.g., Tensor Flow, PyTorch, Scikit-learn).
• Knowledge of big data technologies (e.g., Spark, Hadoop, Snowflake, Databricks) is a plus.
• Soft Skills:
• Strong problem-solving and analytical skills.
• Excellent communication and presentation abilities.
• Ability to work independently and collaboratively in a team environment.
• Preferred Skills
• Experience with cloud platforms such as AWS, Azure, or Google Cloud.
• Knowledge of natural language processing (NLP) or computer vision techniques.
• Understanding of A/B testing and causal inference methodologies.
• Experience in dashboarding tools such as Tableau or Power BI.

Qualifications:

• Bachelor’s degree in data science, Computer Science, Information Systems, or a related field preferred.
• 10+ years of relevant experience.
• 3+ years of experience in implementing data solutions, preferably within the insurance industry.
• Proficiency in data integration tools (e.g., Informatica, Snowflake, Databricks) and analytics platforms (e.g., Tableau, Power BI, Cognos).
• Strong knowledge of insurance operations, including underwriting, claims, and…