Data Scientist

Optomi; New York (NY); On-site; USD 110,000 - 160,000; Full time

Job summary

Optomi is seeking a Data Scientist to support key product analytics initiatives. The candidate will apply SQL, Python, statistical analysis, ML, and experimentation to translate data into actionable product recommendations.

The role offers collaboration in a fast-paced environment with opportunities to shape product strategy and leverage AI/LLM capabilities. Ideal candidates have 2–4 years of experience in data science, strong SQL/Python skills, experience with A/B testing, and familiarity with

Qualifications

• 2–4 years of professional Data Science experience in business analytics or product analytics.
• Strong hands-on experience with SQL and Python for data analysis and modeling.
• Experience with A/B testing, experimentation, and statistical analysis.
• Familiarity with Spark or distributed data processing is a plus.
• Experience with Tableau or similar BI tools.

Responsibilities

• Partner with stakeholders to understand business challenges and define analytical requirements.
• Analyze large datasets using SQL and Python to identify trends and insights.
• Design, execute, and analyze A/B tests to inform product decisions.
• Apply ML techniques to solve business problems and inform strategy.
• Develop dashboards and visualizations to communicate findings.

Job description

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.