Data Scientist

Data Scientist (SQL, Python, A/B Testing)

Optomi, in partnership with our client, a globally established technology organization known for building innovative products and shaping outstanding customer experiences, is seeking a Data Scientist to support key business-facing product analytics initiatives. The ideal candidate will bring hands-on experience using SQL, Python, statistical analysis, machine learning, and experimentation to solve complex business problems and translate data into actionable product suggestions. This opportunity offers work in a highly collaborative, fast-paced environment where data science has a meaningful impact in shaping product strategy, improving customer experiences, and identifying opportunities to apply emerging AI/LLM capabilities.

What the right candidate will enjoy!

• Working on high-visibility product analytics initiatives for a globally recognized technology organization whose products and services are used by millions of customers.
• Using data science, experimentation, and statistical analysis to influence real product and business decisions rather than working solely on theoretical or model-building projects.
• Working with large, complex datasets and modern data technologies to uncover insights and solve challenging business problems.
• Designing and analyzing A/B tests that directly inform product improvements, customer experiences, and business strategies.
• Exploring emerging AI and LLM capabilities and helping identify opportunities to apply them to real-world business and product challenges.
• Collaborating with talented, cross-functional teams and translating analytical findings directly to business and product stakeholders.
• Working in a fast-paced environment where curiosity, critical thinking, innovation, and a willingness to challenge assumptions are highly valued.
• Having the opportunity to take ownership of projects and work through ambiguous problems with a high degree of independence.
• Combining technical data science skills with business understanding to turn complex data into clear, actionable recommendations.
• Gaining exposure to sophisticated product analytics, experimentation, machine learning, and emerging technology initiatives within a large-scale global organization.
• Working alongside professionals who value thoughtful problem-solving, strong communication, attention to detail, and measurable business impact.

Experience of the right candidate:

• 2–4 years of professional experience in Data Science, with experience supporting business-facing analytics, product analytics, or a similar environment.
• Strong hands-on experience with SQL and Python for data analysis, statistical modeling, and solving business problems.
• Experience working with large and complex datasets, preferably within big data environments.
• Experience with Spark or other distributed data processing technologies strongly preferred.
• Hands-on experience designing, executing, and analyzing A/B tests and other experimentation initiatives.
• Strong understanding of statistical analysis and experience applying machine learning techniques to large datasets.
• Experience using data science to generate actionable business insights and recommendations.
• Understanding of AI and LLM capabilities, with the ability to identify and recommend practical business use cases.
• Experience with financial products or within the financial services industry.
• Experience with Tableau or similar business intelligence and data visualization tools.
• Strong mix of technical data science expertise and business acumen.
• Strong critical thinking and problem-solving skills with the ability to approach ambiguous business problems analytically.
• Strong communication and presentation skills, including the ability to explain technical findings to non-technical stakeholders.
• Ability to work independently, take ownership of projects, and operate effectively with limited supervision.

Responsibilities of the right candidate:

• Partner with business stakeholders and product teams to understand business challenges, define analytical requirements, and identify opportunities where data science can drive impact.
• Analyze large and complex datasets using SQL, Python, and other data science tools to identify trends, patterns, and actionable insights.
• Design, execute, and analyze A/B tests and other experimentation initiatives to evaluate product and business decisions.
• Apply statistical analysis and machine learning techniques to solve business problems and support data-driven decision-making.
• Develop analytical approaches and models that translate business questions into measurable outcomes.
• Evaluate product and business performance and provide recommendations based on data-driven findings.
• Identify opportunities to leverage AI and LLM capabilities to improve products, processes, customer experiences, or business outcomes.
• Work with large-scale data environments and leverage Spark or similar technologies when appropriate.
• Develop dashboards, visualizations, and analytical outputs using Tableau or similar BI tools to communicate insights effectively.
• Present analytical findings, recommendations, and business implications to technical and non-technical stakeholders.
• Collaborate cross-functionally with product, business, analytics, and technical teams to drive data-informed initiatives.
• Translate complex statistical and technical concepts into clear business recommendations.
• Work through ambiguous problems, determine appropriate analytical approaches, and drive projects from concept through delivery.
• Communicate project findings and recommendations clearly through presentations, documentation, and stakeholder discussions.
• Stay current with emerging data science, AI, and LLM capabilities and assess how they can be applied to business and product analytics.