Senior Product Data Analyst

Azenta Life Sciences•Burlington, MA

1d•$107,000 - $134,000•Hybrid

About The Position

Azenta Inc. At Azenta, new ideas, new technologies and new ways of thinking are driving our future. Our customer focused culture encourages employees to embrace innovation and challenge the status quo with novel thinking and collaborative work relationships. All we accomplish is grounded in our core values of Customer Focus, Achievement, Accountability, Teamwork, Employee Value and Integrity. We are hiring a Senior AI Product Data Analyst to drive actionable insights, shape product strategy, and accelerate the adoption of AI-enabled decision making across digital products and customer experiences. As a core member of the Product organization, this role will partner with Product Management, UX, Engineering, Data, and Business stakeholders to define success metrics, measure user behavior, evaluate AI-enabled features, and uncover opportunities where analytics, machine learning, automation, and generative AI can deliver measurable business value. This individual will combine strong analytical expertise with modern AI capabilities to transform data into insights, predictions, recommendations, and intelligent product improvements.

Requirements

5+ years in data, product, business analytics, or digital analytics roles with measurable impact on digital product performance.
Proficiency in SQL and Python for querying, analysis, automation, statistical analysis, and scalable analytical workflows.
Strong skills in Power BI, Tableau, or similar tools for dashboard creation, reporting, measurement frameworks, and executive-ready insights.
Experience conducting behavioral, product, funnel, retention, cohort, journey, and experimentation analysis using large-scale data.
Experience applying machine learning, predictive analytics, advanced statistical methods, or AI-assisted analytics to solve business or product challenges.
Working knowledge of machine learning concepts, model evaluation techniques, predictive modeling approaches, and responsible use of AI-generated insights.
Ability to critically evaluate model outputs and AI-generated recommendations, validate results against source data, and clearly communicate assumptions, limitations, and business implications.
Excellent communication and data storytelling skills, with experience presenting to product, business, technical, and executive stakeholders.
Experience defining metrics, synthesizing complex data sources, and delivering insight in agile, cross-functional teams.
Familiarity with data governance, data privacy, security controls, metadata management, and responsible AI principles.

Nice To Haves

Experience designing analytics solutions for AI-enabled products, digital platforms, intelligent automation, or customer-facing digital experiences.
Experience using Generative AI or LLM-based tools such as Microsoft Copilot, Azure OpenAI, ChatGPT Enterprise, Claude, or equivalent technologies to support analytics, insight generation, automation, or product discovery.
Familiarity with prompt engineering, AI-assisted workflow design, natural-language analytics, or automated insight-generation approaches.
Exposure to machine learning use cases such as forecasting, recommendation engines, anomaly detection, churn/retention modeling, personalization, clustering, propensity scoring, or prioritization models.
Experience measuring and optimizing AI feature adoption, recommendation quality, conversational AI performance, copilots, personalization capabilities, or model usefulness.
Experience with Azure AI services, Azure OpenAI, Databricks, Snowflake, Microsoft Fabric, or other cloud-scale analytics environments.
Knowledge of model monitoring, model lifecycle management, experimentation frameworks, explainability, bias mitigation, and human-in-the-loop validation.
Experience in enterprise SaaS, B2B, digital commerce, life sciences, healthcare technology, or regulated environments.
Familiarity with experimentation platforms such as Adobe Target, Optimizely, or similar tools.
Exposure to compliance frameworks such as GDPR, 21 CFR Part 11, or relevant data governance and privacy standards.

Responsibilities

Define, influence, and maintain core product KPIs that inform feature adoption, user engagement, retention, customer experience, and business value.
Conduct deep-dive analyses on product performance, user behavior, funnel conversion, feature usage, and digital journey effectiveness.
Identify opportunities to apply Artificial Intelligence (AI), Machine Learning (ML), automation, and Generative AI to improve digital products, customer experiences, insight generation, and operational efficiency.
Design and measure AI-specific KPIs including adoption, utilization, accuracy, relevance, usefulness, productivity gains, customer satisfaction, user trust, and business impact.
Develop predictive and prescriptive analytical models to support product prioritization, customer behavior forecasting, segmentation, roadmap planning, and opportunity scoring.
Analyze results from A/B and multivariate tests to evaluate product experiments, AI-enabled features, recommendation logic, and performance impact.
Build and maintain self-serve dashboards in Power BI or Tableau to support product transparency, executive visibility, and data-driven decision-making.
Use AI-assisted analytics tools and Large Language Models (LLMs) to accelerate data exploration, root-cause analysis, summarization, narrative development, and decision support.
Partner with Engineering and Data teams to validate data pipelines, telemetry, model outputs, feature performance, and AI-driven recommendations.
Communicate complex findings through visual storytelling, concise executive summaries, and product recommendations that translate data into action.
Support data definition, documentation, metadata alignment, and analytics governance across Product, UX, Engineering, and Business stakeholders.
Promote responsible AI practices, including explainability, human oversight, data privacy, security, bias awareness, governance, and validation of AI-generated outputs.
Build reusable analytics assets, prompts, dashboards, measurement frameworks, and documentation that help teams use AI responsibly and consistently.