Data Scientist - Global Insurance firm

Randstad is partnered with global Insurance firm, and we are looking for a Lead Data Scientist to drive innovative approaches in understanding, interacting, and serving their customers by optimizing analytics to derive actionable business insights. The successful candidate will work closely with business stakeholders, mostly in Japanese, to understand their requirements and business background, and lead data analytics initiatives from conception to delivery.

Responsibilities:

• Collaborate with business stakeholders to gather requirements, understand business context, and derive data-driven solutions.
• Lead and oversee data analytics initiatives, including planning, executing, and delivering high-quality analytical solutions.
• Work in close collaboration with the data platform team and other IT teams, identifying dependencies and resolving blockers to ensure smooth development processes.
• Demonstrate deep understanding of analytics initiatives, machine learning model development, deployment, and timeline management.
• Utilize CI/CD, MLOps, Azure or similar cloud platforms, and DevOps to streamline development and deployment processes.
• Utilize hands-on skills with SQL, Python, Pandas, PySpark, AzureML, and PowerBI to develop and implement data models and analytics.
• Apply expertise in financial or insurance domains, particularly in operations and marketing, to ensure successful project outcomes.
• Prepare detailed documentation for analytics and ML initiatives, including project deliverables.
• Communicate effectively with team members and leadership through presentations on analytics and machine learning requirements, progress, and outcomes.

Experience:
- 5+ years of experience in advanced analytics roles within companies, delivering successful business outcomes.
- Proven track record of success in advanced analytics roles, with strong leadership skills.

Business Knowledge/Technical Skills:
- Proficiency in quantitative methods and business analytics, with strong problem-solving skills.
- Ability to build and apply data models using machine learning techniques such as classification, regression, clustering, time-series analysis, and dimensionality reduction.
- Competent in programming with tools like Python and SQL.
- Strong understanding of data visualization tools, especially PowerBI.
- Experience with CI/CD, MLOps, and cloud platforms like Azure for
deploying analytics and ML solutions.
- Hands-on experience with data analytics tools and libraries such as Pandas, PySpark, and AzureML.
- Background in financial or insurance domain, particularly in operations and marketing.