Data Analyst transforming data into insights for AI - first Hiring System at Alva Labs. Collaborating across teams to support evidence - based hiring decisions and drive business outcomes.
About the role
- Senior Data Analytics Translator aligning data science capabilities with business objectives at Ascensus. Collaborating with stakeholders to drive marketing growth and analytics outcomes.
Responsibilities
- Use Case Ownership & Lead: Own portfolio of related use cases in partnership with senior leaders.
- Initial responsibilities to accelerate Retirement marketing analytics and growth.
- Partner with stakeholders to identify, prioritize, and define high-value problems that can be solved using AI and advanced analytics.
- Convert business goals into technical requirements for data scientists and engineers, ensuring the analytics deliverables drive solutions to the core business problem.
- Guide the technical work to optimize impact on business problems.
- Synthesize complex model results into clear, actionable recommendations for non-technical stakeholders through compelling data storytelling.
- Effectively right size communication content to varying stakeholders from all levels of leadership.
- Optimize use of AI and other analytics tools to improve reporting and communications that best inform.
- Use BI tools to turn raw technical outputs into executive-level narratives.
- Maintain familiarity with external research and benchmarks, qualitative research and analytics deliverables beyond the specific project to consider and coalesce relevant insights into the data story and recommendations.
Requirements
- Domain Expertise: Deep knowledge of retirement industry and critical metrics (e.g., distribution, sales, revenue, customer retention) and how they impact the company’s profit and loss.
- If not retirement, an adjacent financial industry with proven success incorporating data insights in growth initiatives.
- Technical Fluency: A strong understanding of quantitative analytics, including the ability to identify which business problems fit specific models, multi-variate testing and/or inclusion of qualitative analysis to help lead solutioning and execution to results.
- Excellent analytical skills.
- Project Management: Proficiency in data science analytics cycle management and agile frameworks that benefit a full feedback loop.
- Relationship building and influence: Ability to handle more complex organizational politics and cross-functional conflicts.
- Exceptional stakeholder management to build trust, influence solutions and ensure continued progress to achieve goals.
- Communication: Excellent oral and written communication skills providing examples of how data visualization and storytelling were used to inform resulting in actionable decisions and valuable outcomes.
- Education: A bachelor's or master's degree in a STEM field (Statistics, Mathematics, Computer Science) or Business with a heavy focus on data literacy.
- 5+ years of relevant experience in an analytics translator role or related experience.
- Experience must include projects that span multiple departments frequently presenting to VP and executive level leadership.
- Tools and technology experience necessary for role Business Intelligence and visualization tools ideally with Microsoft solutions; or transferrable experience with other tools AI-Powered decision support and efficiency.
Benefits
- This position is expected to identify thematic issues and opportunities across projects, bringing insights from other projects to benefit current project goals.
- Ensure defined success metrics align with business goals.
- Drive optimal business decisions by providing quantitative and qualitative data analysis, forecasts, propensity observations, predictions, insights and trends to decision makers.
- Be a proponent of personalization and other tactics to optimize outcomes incorporating new technologies and methods in actions.
- Manage the analytics life cycle for assigned projects and programs from ideation to production, including tracking KPIs to validate the business impact of data solutions.
- Own timeline and deliverables of the projects undertaken with focus on stakeholder satisfaction, success metrics and on time delivery.
- Ensure technical associates stay aligned with business goals in their efforts.
- Assist with project definition and planning.
- Rely on a multi-layered tech stack that balances deep data exploration with high-level business communication and effective project oversight.
- Drive organizational adoption of analytical tools.
- Foster a data-driven culture by educating users on how to interpret and execute on insights.
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