Product Analyst, AlphaSense
Strategy & AI
$158-217k
Market intelligence and search platform
Be an early applicant
Market intelligence and search platform
1001+ employees
Be an early applicant
$158-217k
1001+ employees
Company mission
AlphaSense's mission is to organise the world’s business information and leverage technology to enable companies to make smarter, faster, and more confident decisions.
Role
Who you are
- Deep Product & Business Intuition: Demonstrated ability to understand product strategy and key metrics
- Experience: 8+ years of experience working in Product Analytics, Data Analytics, or equivalent roles
- Communication & Influence: Exceptional written and verbal communication skills, with a proven track record of presenting complex data insights clearly and persuasively to both technical and non-technical stakeholders. Proven ability to build strong working relationships and influence decision-making across cross-functional teams, particularly with Product Management
- The "Stats-First" Mindset: As AI makes querying easier, knowing which statistical tests to apply and how to interpret the noise is your superpower
- Technical Fluency (SQL & AI): Expert-level SQL is a must. You don't need to be a core software engineer, but you must be comfortable using Python and working with APIs, LLMs, and agentic frameworks
- Curiosity & Adaptability: The AI tooling landscape changes rapidly. You are the kind of person who actively explores new frameworks, and experiments with how to apply them to business problems
- Collaborative Leadership: Ability to work seamlessly with a highly technical remote data team (India) while acting as the strategic face of data for the product leadership team (NYC)
- Location & Alignment: Ability to effectively collaborate and interact in real-time with Product teams primarily based in NYC, while also being able to work effectively with a team in India
What the job involves
- AlphaSense is seeking a highly analytical, entrepreneurial Product Analyst, Strategy & AI to serve as the analytical engine for our Product Management team and own our most important product analytics questions
- Our foundational data engineering and reporting are expertly managed by our technical team in India
- We are hiring this NYC-based role to be the "connective tissue" between our raw data and our strategic product decisions
- Deep Strategic Analysis: Tackle our most complex product questions (e.g., understanding the true impact of our GenAI features on WAU/DAU retention and user habit formation)
- AI-Native Data Democratization: Architect the infrastructure that allows our PMs to query our BigQuery database using natural language. You will be the tip of the spear in transitioning our product analytics model from a "request-and-wait" dashboard culture to a real-time, AI-empowered ecosystem
- Be the Strategic Co-Pilot for PMs: Co-locate with the Product team to understand the business deeply. Proactively look across all product features to identify trends, drop-offs, and opportunities that go beyond feature-by-feature reporting. Lead the effort to translate complex data analysis into clear, concise, and compelling narratives that drive key business decisions and product feature development decisions
- Deep-Dive Analysis: Conduct and oversee comprehensive, complex analyses of product usage and adoption. Go beyond surface-level metrics to identify root causes for trends
- Project Oversight: Act as a thought partner to Product leadership, proactively identifying opportunities and risks through data analysis. Collaborate closely with Product Managers to define key performance indicators (KPIs), establish success metrics for new features, and provide ongoing, proactive insights into product performance
- Solve High-Stakes Business Questions: Use advanced statistical methods (e.g., cohort analysis, propensity matching, causal inference, etc.) to answer critical executive and investor questions—such as whether new product capabilities are actually inflecting long-term user retention
- Build the AI-to-Data Bridge: Wire up our BigQuery data warehouse to modern LLMs using standard protocols (like Model Context Protocol / MCP) or native cloud AI tooling
- Design the Semantic Layer: Partner with our Data Engineering team in India to ensure our data schemas are "AI-ready" (e.g., clean, well-aliased, and structured)
Our take
Manual research can be a serious pain: financial analysts spend hours every week searching through PDFs one keyword at a time, just to find a single usable insight. To remedy this, AlphaSense has built a smart search engine that slashes research time for analysts, using thousands of sources and a semantic indexing layer that can effectively query finance-specific language.
As a disruptor and market leader in this field, AlphaSense has few direct competitors, being most closely compared to Wolfram Alpha or LexisNexis. It helps that its tools are undoubtedly effective and valued by knowledge professionals: the company counts about 85% of the S&P 100 as customers and is trusted by J.P.Morgan, Microsoft, Credit Suisse and Google, among other major enterprises.
Having raised a large amount of funding over 2022 and 2023, AlphaSense has moved quickly to double down on its expansion in European markets, opening an office in Berlin to support this. With 2024 seeing investment from Singaporean EDBI, it has now set its sights on the Asia-Pacific region. As business intelligence will remain key for enterprises as they compete for market share, AlphaSense is well-placed for additional future growth.
Freddie
Company Specialist at Welcome to the Jungle
Insights
13% employee growth in 12 months
Company
Funding (last 2 of 11 rounds)
Jun 2024
$650m
LATE VC
Sep 2023
$150m
SERIES E
Total funding: $1.3bn
Company benefits
- Work from home opportunities
Company HQ
Union Square, New York, NY
Leadership
Jaakko Kokko
(Co-Founder & CEO)Co-founder of Silecs (materials used in the microelectronics industry).
Raj Neervannan
(Co-Founder & CTO)Experienced executive. Previously CTO at Mastek and ChoicePoint, and CEO at Framesoft. Has an MBA from Wharton.
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