Data Analyst, Product Strategy & AI

📍 Job Overview

Job Title: Data Analyst, Product Strategy & AI

Company: AlphaSense

Location: New York, New York, United States

Job Type: Full-time

Category: Product Operations / Data Analytics / Strategy

Date Posted: April 27, 2026

Experience Level: Mid-Senior Level (5-10 years)

Remote Status: On-site

🚀 Role Summary

• This role is pivotal in bridging raw data with strategic product decisions, acting as the analytical engine for the Product Management team.

• Focuses on deep strategic analysis of product impact, particularly concerning AI features and user retention, and democratizing data access through AI-native tools.

• Requires expert-level SQL proficiency and hands-on experience with Python, APIs, LLMs, and agentic frameworks to build an AI-powered analytics ecosystem.

• The position demands a strong "stats-first" mindset to interpret complex data and influence product development through compelling, data-driven narratives.

📝 Enhancement Note: This role sits at the intersection of Product Management, Data Analytics, and AI. While not a traditional Sales or Revenue Operations role, its emphasis on data-driven decision-making, metrics, and understanding user behavior to drive business outcomes aligns strongly with the operational focus of GTM functions. The "democratization" aspect suggests a need for robust data governance and accessibility, key tenets in operational efficiency.

📈 Primary Responsibilities

• Serve as the strategic co-pilot for Product Managers, deeply understanding business needs and proactively identifying trends, drop-offs, and opportunities across all product features.

• Conduct and oversee comprehensive, complex analyses of product usage, adoption, and retention, going beyond surface-level metrics to identify root causes and actionable insights.

• Translate complex data analysis into clear, concise, and compelling narratives that drive key business decisions and product feature development.

• Define key performance indicators (KPIs), establish success metrics for new features, and provide ongoing, proactive insights into product performance.

• Utilize advanced statistical methods (e.g., cohort analysis, propensity matching, causal inference) to answer critical executive and investor questions regarding product impact on long-term user retention and habit formation.

• Architect and implement the infrastructure to connect the BigQuery data warehouse to modern LLMs using standard protocols or native cloud AI tooling, enabling natural language querying for Product Managers.

• Partner with the Data Engineering team in India to ensure data schemas are "AI-ready," meaning clean, well-aliased, and appropriately structured for AI-driven analysis and democratization.

• Act as a thought partner to Product leadership, proactively identifying opportunities and risks through data analysis.

📝 Enhancement Note: The emphasis on "AI-Native Data Democratization" and "AI-to-Data Bridge" construction is a significant differentiator. This implies the need for the candidate to not only analyze data but also to design and implement systems that empower non-technical users to access and interpret data, a critical component of operational efficiency and self-service analytics within product teams.

🎓 Skills & Qualifications

Education:

Experience:

• 5-10 years of experience in data analysis, business intelligence, or product analytics roles, with a strong focus on product strategy and user behavior analysis.

• Demonstrated experience in translating complex data findings into actionable business insights and strategic recommendations.

Required Skills:

• Expert-level SQL: Essential for querying and manipulating large datasets in BigQuery.

• Python Proficiency: For scripting, data manipulation, API interaction, and potentially building analytical models.

• Statistical Analysis: Strong understanding and practical application of statistical methods such as cohort analysis, propensity matching, causal inference, A/B testing, and regression analysis.

• Product Analytics: Experience defining and tracking product KPIs, analyzing user behavior, and understanding product adoption and retention drivers.

• AI/LLM Familiarity: Comfortable working with Large Language Models (LLMs), agentic frameworks, and APIs to enable natural language data querying.

• Data Visualization & Storytelling: Ability to create clear, compelling visualizations and narratives to communicate complex insights to both technical and non-technical audiences.

• Communication & Influence: Exceptional written and verbal communication skills, with a proven track record of presenting complex data insights clearly and persuasively to stakeholders, including senior leadership.

• Cross-functional Collaboration: Ability to build strong working relationships and influence decision-making across diverse teams, particularly Product Management and Data Engineering.

Preferred Skills:

• Experience with cloud data warehouses like Google BigQuery.

• Familiarity with data modeling and schema design, particularly for AI readiness.

• Experience with data democratization initiatives or self-service analytics platforms.

• Knowledge of Model Context Protocol (MCP) or similar data interaction standards.

• Experience working with remote, international data engineering teams.

📝 Enhancement Note: The "Stats-First Mindset" and "Curiosity & Adaptability" are crucial for this role, highlighting the need for a candidate who isn't just a technician but a strategic thinker capable of navigating the rapidly evolving AI landscape and applying statistical rigor to novel problems.

📊 Process & Systems Portfolio Requirements

Portfolio Essentials:

• Case Studies in Product Impact: Showcase at least 2-3 detailed case studies demonstrating how your data analysis directly influenced product strategy, feature development, or user retention. Clearly articulate the problem, your analytical approach, the insights derived, and the measurable business outcomes.

• Demonstration of Statistical Rigor: Include examples of complex statistical analyses performed (e.g., causal inference models, propensity matching) and explain the methodology, assumptions, and interpretation of results. Highlight how these advanced techniques provided insights beyond basic reporting.

• Data Democratization/AI Integration Examples: If possible, showcase projects where you enabled easier data access for non-technical stakeholders, or any experience integrating AI/LLMs with data sources for analysis or querying. This could include examples of building semantic layers or designing user-friendly data interfaces.

• SQL Proficiency Showcase: Provide examples demonstrating advanced SQL querying capabilities, including complex joins, window functions, and CTEs, preferably applied to large datasets.

Process Documentation:

• Analytical Workflow Design: Document a typical workflow for tackling a complex product analytics question, from understanding the business need to delivering actionable insights. This should cover data exploration, hypothesis formulation, methodology selection, analysis execution, and insight synthesis.

• Collaboration Protocols: Illustrate how you collaborate with Product Managers, engineers, and other stakeholders to define requirements, validate findings, and drive action based on data. Detail your approach to managing feedback and iterating on analyses.

• Data Quality & Readiness: Describe your process for assessing data quality and preparing datasets for analysis, especially with an eye towards AI readiness (e.g., schema design, data aliasing, data cleaning strategies).

📝 Enhancement Note: Given the role's focus on AI and data democratization, a portfolio demonstrating the ability to build systems that empower others with data, alongside traditional analytical rigor, will be highly valued. Emphasis should be placed on the impact of the analysis and the strategic application of insights.

💵 Compensation & Benefits

Salary Range: $158,000 - $217,000 USD per year

Benefits:

• Performance-based bonus: Incentive tied to individual and company performance, reflecting the data-driven culture.

• Equity: Opportunity to own a stake in the company's growth, common in fast-paced tech environments.

• Generous benefits program: Comprehensive health, dental, and vision insurance, retirement savings plans (e.g., 401k), paid time off, and potentially other perks like wellness stipends or professional development allowances.

Working Hours: Approximately 40 hours per week.

• While a standard 40-hour work week is expected, the role likely requires flexibility to engage in real-time collaboration with teams in different time zones (NYC and India) and to respond to critical business needs as they arise.

📝 Enhancement Note: The salary range is competitive for a Data Analyst role with significant strategic and AI components in New York City. The benefits package is standard for a growth-stage tech company, emphasizing performance and long-term commitment.

🎯 Team & Company Context

🏢 Company Culture

Industry: Market Intelligence / SaaS / AI-driven Insights

Company Size: Over 2,000 employees (as of the provided data). This indicates a well-established company with significant resources and a global presence, likely with mature processes but still retaining some aspects of a growth-stage environment.

Founded: 2011. This suggests a company with a solid track record and established market position.

Team Structure:

• The Data Analyst will be embedded within the Product Management team, primarily based in NYC, acting as their dedicated analytical resource.

• There is a close, collaborative relationship expected with a highly technical Data Engineering team located in India, requiring effective cross-cultural and remote communication.

Methodology:

• Data-Driven Decision Making: The company culture strongly emphasizes using data to inform strategic decisions, particularly in product development and market positioning.

• AI Integration: A core strategy involves leveraging AI to enhance product capabilities and user experience, as evidenced by the focus on GenAI features and AI-native analytics.

• Agile Product Development: Given the tech industry context, an agile methodology for product development and iteration is likely.

• Global Collaboration: The company operates with distributed teams, necessitating strong communication protocols and asynchronous collaboration practices.

Company Website: www.alpha-sense.com

📝 Enhancement Note: The company's mission to "remove uncertainty from decision-making" through AI-driven market intelligence, combined with the acquisition of Tegus, highlights a focus on sophisticated data analysis and actionable insights. The presence of a large, global workforce suggests a structured environment where clear communication and process adherence are critical.

📈 Career & Growth Analysis

Operations Career Level: This role is positioned as a critical, highly specialized individual contributor within the Product organization, functioning at a senior analyst or "lead" level for product analytics. It's not a traditional "operations" role in the GTM sense but operates with a strong operational mindset focused on product efficiency, user engagement, and data-driven strategy.

Reporting Structure: The Data Analyst will likely report to a Director or VP of Product Management in NYC. They will also have a strong dotted-line reporting or collaborative relationship with the Data Engineering team in India.

Operations Impact: This role has a direct impact on product strategy, feature prioritization, and the overall user experience by providing critical insights into product performance, user behavior, and the effectiveness of AI initiatives. The success of AI features and user retention can significantly influence revenue and market share.

Growth Opportunities:

• Specialization in AI Product Analytics: Deepen expertise in applying AI/LLMs to product analytics, becoming a go-to expert in this emerging field.

• Product Strategy Influence: Grow into a more strategic role, shaping the product roadmap and contributing to high-level business decisions based on advanced analytics.

• Leadership in Data Democratization: Lead initiatives to build and scale self-service analytics capabilities for Product teams, potentially managing a small team or defining best practices.

• Transition to Product Management: The deep immersion with the Product team and understanding of product strategy could serve as a pathway into a Product Manager role.

• Cross-functional Leadership: Develop strong leadership skills in managing relationships and driving outcomes with distributed, international teams.

📝 Enhancement Note: This role offers a unique growth trajectory for data professionals interested in the strategic application of AI within a product context. It bridges the gap between deep technical analysis and direct business impact, offering significant potential for career advancement within a leading market intelligence company.

🌐 Work Environment

Office Type: The role is designated as "On-site" in New York City, indicating a requirement for physical presence in the company's NYC office. This suggests a collaborative, in-person work environment for the Product team.

Office Location(s): New York, New York, United States.

Workspace Context:

• Collaborative Environment: The role requires close co-location with the Product Management team, fostering real-time brainstorming, feedback, and strategic alignment.

• Technology Access: Expect access to robust data infrastructure (BigQuery), modern AI tooling, and the necessary software and hardware to perform complex data analysis and development.

• Cross-functional Interaction: Frequent interaction with Product Managers, engineers, designers, and potentially marketing and sales teams to gather context, share insights, and drive alignment.

• Global Connectivity: While based in NYC, the role necessitates effective remote collaboration tools and practices to connect with the Data Engineering team in India.

Work Schedule: Standard full-time hours (approx. 40 hours/week) with potential for flexibility.

• The need to work effectively with teams in India may require occasional adjustments to standard working hours for critical meetings or project milestones, emphasizing asynchronous communication and clear documentation.

📝 Enhancement Note: The "On-site" designation in NYC suggests a preference for in-person collaboration, which can be highly beneficial for strategic roles requiring close alignment with leadership and product teams. However, the global nature of the data engineering team means strong remote collaboration skills are still paramount.

📄 Application & Portfolio Review Process

Interview Process:

• Initial Screening: A recruiter will likely conduct an initial phone screen to assess basic qualifications, interest, and cultural fit.

• Technical Assessment (SQL/Python): Expect a rigorous technical assessment, likely involving a live coding session or take-home assignment focusing on complex SQL queries, data manipulation with Python, and potentially basic statistical concepts.

• Case Study Presentation: A significant part of the interview process will involve presenting a pre-prepared portfolio case study. This will demonstrate your ability to analyze complex problems, derive insights, and communicate findings effectively. Be prepared to discuss your methodology, assumptions, and the impact of your work.

• Product Strategy & AI Deep Dive: Interviews with Product Managers and potentially senior leadership will focus on your understanding of product strategy, key metrics, user behavior, and your approach to leveraging AI for analytics. Questions may explore how you'd tackle specific product challenges.

• Cross-functional Collaboration Simulation: You may be asked to participate in a simulated scenario or discussion to assess your ability to collaborate effectively with remote teams and influence stakeholders.

• Final Round: Typically involves meetings with senior leadership (e.g., VP of Product, Head of Data) to discuss overall fit, strategic thinking, and long-term vision.

Portfolio Review Tips:

• Focus on Impact: For each case study, clearly articulate the business problem, your specific contribution, the insights you uncovered, and the quantifiable impact (e.g., % increase in retention, reduction in churn, revenue lift).

• Showcase Strategic Thinking: Don't just present data; explain the "why" behind your analysis and how it informed strategic decisions. Demonstrate your ability to connect data to business objectives.

• Highlight Technical Prowess: Clearly outline the tools and techniques used (SQL, Python, specific statistical methods, AI/LLM tools). For SQL, ensure complexity and efficiency are evident.

• Address AI/Democratization: If you have relevant experience, explicitly showcase projects that demonstrate your ability to build systems for data access or integrate AI for analytical purposes.

• Structure for Clarity: Organize your portfolio logically. Use clear headings, concise descriptions, and compelling visualizations. Be prepared to walk through your work methodically.

• Tailor to the Role: Emphasize examples relevant to product analytics, user behavior, retention, and AI applications, as these are core to the job description.

Challenge Preparation:

• SQL Mastery: Practice complex SQL queries on large datasets. Understand performance optimization techniques.

• Python for Data: Refresh your skills in data manipulation libraries (Pandas), and be ready to discuss API usage and potentially basic scripting for automation.

• Statistical Concepts: Review core statistical concepts relevant to product analysis (cohorts, A/B testing, causal inference). Be prepared to explain when and why to use each.

• Product Metrics: Familiarize yourself with common product KPIs (WAU/DAU, retention curves, conversion rates, churn) and how to analyze them.

• AI/LLM Application: Think about how LLMs can be applied to data analysis and querying. Consider potential challenges and solutions.

• Communication Practice: Rehearse explaining technical concepts and data insights to non-technical audiences. Practice structuring your thoughts for clear and persuasive delivery.

📝 Enhancement Note: The emphasis on AI and data democratization suggests that candidates who can demonstrate not only analytical skills but also system-building capabilities for data access will have a distinct advantage. The interview process will likely be rigorous, testing both technical depth and strategic product thinking.

🛠 Tools & Technology Stack

Primary Tools:

• BigQuery: The core data warehouse. Expertise in querying and potentially optimizing BigQuery is essential.

• SQL: The primary language for data extraction and manipulation in BigQuery.

• Python: For scripting, data analysis, automation, API interactions, and potentially working with LLMs. Libraries like Pandas, NumPy, and Scikit-learn are likely relevant.

• APIs: For integrating different systems and potentially interacting with LLM services.

• LLMs (Large Language Models): Familiarity with working with models like GPT, Claude, or similar, and understanding their application in data analysis and querying.

• Agentic Frameworks: Experience with frameworks that enable AI agents to perform tasks or complex workflows, potentially involving data retrieval and analysis.

Analytics & Reporting:

• Data Visualization Tools: While not explicitly stated, expect to use tools for creating dashboards and reports. This could include Tableau, Looker, Power BI, or internal tools. The role emphasizes insights over traditional dashboards, but visualization skills are key for communication.

• Statistical Software/Libraries: Proficiency in Python libraries (Pandas, SciPy, Statsmodels) or potentially R for advanced statistical analysis.

CRM & Automation:

• While not directly stated as a primary focus, understanding how product analytics interfaces with CRM or marketing automation platforms (e.g., Salesforce, HubSpot) can be beneficial for context on user journeys and customer data.

📝 Enhancement Note: The explicit mention of BigQuery, Python, APIs, LLMs, and Agentic Frameworks highlights a modern, AI-centric data stack. Candidates should be comfortable working within cloud environments and leveraging cutting-edge AI technologies for analytical purposes.

👥 Team Culture & Values

Operations Values:

• Data-Driven: Decisions are made based on rigorous analysis and insights, not just intuition. This role is central to upholding this value for the product organization.

• Customer-Centric: Understanding user behavior and product adoption is key to delivering value. The analysis performed directly impacts the user experience.

• Innovation & Agility: Embracing new technologies (like AI) and adapting quickly to market changes and analytical challenges is paramount.

• Collaboration & Transparency: Working effectively across teams (Product, Engineering, global teams) and sharing insights openly is crucial for success.

• Impact & Accountability: Focusing on delivering measurable business outcomes and taking ownership of analytical projects from start to finish.

Collaboration Style:

• Embedded Partnership: The Data Analyst will work closely and collaboratively with Product Managers, acting as an integrated member of the product team.

• Cross-functional Influence: A key aspect is influencing decision-making across different departments through compelling data narratives and strategic recommendations.

• Remote Team Synergy: Requires proactive communication, clear documentation, and cultural sensitivity to build strong working relationships with the Data Engineering team in India, overcoming geographical and time zone differences.

• Feedback Loop: An open feedback culture is expected, where analyses are shared, reviewed, and iterated upon collaboratively.

📝 Enhancement Note: The company's culture appears to value innovation, data-informed strategy, and strong cross-functional teamwork. Success in this role will depend on the ability to integrate seamlessly with the Product team while effectively managing relationships with a remote technical counterpart.

⚡ Challenges & Growth Opportunities

Challenges:

• Bridging Technical Teams: Effectively translating business needs from the NYC Product team to the technical Data Engineering team in India, and vice-versa, managing potential communication gaps and time zone differences.

• Rapidly Evolving AI Landscape: Staying current with fast-changing AI tools, frameworks, and methodologies to ensure the company is leveraging the most effective solutions for data democratization and analysis.

• Translating Complex Data to Actionable Insights: The core challenge of any data role, but amplified here by the complexity of product strategy and AI feature impact, requiring sophisticated analysis and clear communication.

• Defining and Measuring "True" Impact: Quantifying the long-term impact of AI features on user retention and habit formation requires robust analytical methodologies and careful interpretation of results.

• Balancing Deep Analysis with Self-Service Enablement: Juggling in-depth strategic analysis with the architectural work required to empower PMs to query data themselves.

Learning & Development Opportunities:

• Cutting-Edge AI Expertise: Become a specialist in applying AI and LLMs to product analytics and data democratization, a highly in-demand skill set.

• Product Strategy Acumen: Gain deep understanding of product development lifecycles, user behavior drivers, and strategic decision-making processes within a leading SaaS company.

• Global Collaboration Skills: Enhance abilities in cross-cultural communication, remote team management, and asynchronous collaboration.

• Advanced Analytics Techniques: Opportunity to apply and refine sophisticated statistical methods like causal inference and propensity matching on real-world product data.

• Influence and Leadership: Develop strong influencing skills to drive data-informed decisions across the organization and potentially lead initiatives.

📝 Enhancement Note: This role presents significant challenges but offers substantial rewards in terms of skill development and career advancement, particularly for those interested in the cutting edge of AI and product analytics.

💡 Interview Preparation

Strategy Questions:

• "How would you approach measuring the true impact of our new GenAI features on user retention and habit formation?" (Focus on methodology, metrics, and potential pitfalls.)

• "Describe your process for democratizing data access for non-technical stakeholders. What tools or architectures would you consider?" (Highlight your understanding of AI/LLM integration and data governance.)

• "Imagine a key product metric suddenly drops. How would you investigate the root cause, and what steps would you take to communicate your findings and recommendations?" (Demonstrate your analytical process and communication strategy.)

Company & Culture Questions:

• "What interests you most about AlphaSense and our mission?" (Research the company's market, AI focus, and recent acquisitions.)

• "How do you stay updated on the rapidly evolving AI tooling landscape?" (Show your proactive learning approach.)

• "Describe a time you had to influence a decision with data when stakeholders were resistant." (Prepare a STAR method example demonstrating communication and persuasion skills.)

Portfolio Presentation Strategy:

• Start with the "Why": Clearly articulate the business problem or strategic question your case study addresses.

• Detail Your Process: Explain your analytical methodology step-by-step, including data sources, tools used, statistical techniques, and any assumptions made.

• Showcase Insights, Not Just Data: Focus on the key findings and what they mean for the business. Use visualizations effectively to support your narrative.

• Quantify Impact: Explicitly state the business outcomes or recommended actions resulting from your analysis. Use numbers and metrics wherever possible.

• Address Challenges: Be prepared to discuss any challenges you encountered and how you overcame them.

• Connect to AlphaSense: Where possible, subtly relate your experience and insights to AlphaSense's context, its products, and its strategic goals.

📝 Enhancement Note: Be prepared to discuss both your technical skills (SQL, Python, AI/LLM concepts) and your strategic thinking. The interviewers will be looking for someone who can not only crunch numbers but also translate them into actionable product strategy and build the systems to empower others.

📌 Application Steps

To apply for this operations-aligned Data Analyst position:

• Submit your application through the AlphaSense careers portal via the provided link.

• Curate Your Portfolio: Select 2-3 of your strongest case studies that best demonstrate your expertise in product analytics, strategic data analysis, statistical rigor, and ideally, any experience with AI/LLMs or data democratization. Ensure each case study clearly outlines the problem, your methodology, key insights, and measurable business impact.

• Optimize Your Resume: Tailor your resume to highlight keywords from the job description, such as "Product Strategy," "AI," "SQL," "Python," "BigQuery," "Data Analysis," "Statistical Methods," "Causal Inference," "LLMs," and "Data Democratization." Quantify your achievements with specific metrics wherever possible.

• Prepare Your Presentation: Rehearse presenting your portfolio case studies. Focus on clear, concise communication, logical flow, and the ability to answer in-depth questions about your approach and findings. Practice explaining technical concepts to a non-technical audience.

• Research AlphaSense: Thoroughly research AlphaSense's products, market position, mission, recent news (especially the Tegus acquisition), and any publicly available information on their culture and technology stack. Understand their use of AI and market intelligence.

⚠️ Important Notice: This enhanced job description includes AI-generated insights and operations industry-standard assumptions. All details should be verified directly with the hiring organization before making application decisions.