Associate Director, Product Management - Software, Multiomics Data Partnerships

About the role

Element Biosciences develops software and data-focused products for research and commercial teams. The role defines product direction for software and bioinformatics tools and engages in conversations about AI and multiomics.

Data roles turn raw information into decisions. Analysts query databases and build dashboards. Data scientists build models that predict outcomes. Data engineers build the pipelines that move and store data. All three work closely with business teams and need a mix of statistics, coding, and communication. Nearly every modern company runs on data teams, from startups to banks. A strong portfolio of past analyses matters more than degrees in many hiring decisions.

Key facts

What you'll do

Product vision, strategy, and roadmaps for software and bioinformatics tools are defined and updated to respond to user needs.

Feature return is optimized by prioritizing requirements using voice-of-customer data, usage metrics, and business impact, following the same process used by the PM team.

Detailed product requirements are defined and owned, and the role participates in the PM leadership and core teams.

Product-market fit is monitored through usage metrics and customer feedback, with iterative updates based on findings.

Requirements

The posting states a pay range of $180000 to $226000.

The posting states a bachelor's degree requirement. A Life Sciences degree is required with either 8+ years of product management experience or an advanced degree plus 6+ years, including end-to-end ownership of a software or bioinformatics/data product.

Understanding of genomics or multiomics is required to interpret data meaning, beyond mere data structure.

A track record of owning a product area end-to-end and representing it to executive leadership is required.

Proven day-to-day collaboration with R&D and engineering teams on software or bioinformatics products is required.

The role carries partner- and customer-facing conversations about data strategy, business models, and AI independently, representing Element directly in discussions.

Experience structuring or negotiating data licensing, co-development, or platform partnerships in life sciences, healthcare, or a related data-rich industry is required.

Familiarity with applied AI or ML in life-sciences or data-heavy products is required.

Experience in organizations moving from hardware or consumables toward software- or data-inclusive models is required.

Practical notes

The position requires flexibility in working hours to support customers outside regular business hours.

Typical interview steps

Data interviews commonly include a SQL or coding exercise, a statistics question, and a case study. Candidates may be asked to design a metric, interpret an experiment, or build a small model. Some companies give a take-home analysis. Expect questions about past projects and the business impact of your work. Interviewers often evaluate how you communicate uncertainty and business impact, not only the math. Bringing a clean write-up of a past analysis to the interview is well received.

Good to know

Product management in life sciences focuses on translating complex data into actionable product decisions.

Cross-functional collaboration with R&D, engineering, and commercial teams is central to delivering data-driven solutions.

AI and multiomics are shaping how organizations access and monetize data-rich platforms.

Data licensing and partnership models are common in life sciences and healthcare industries.

Success in this role depends on fluency with both product strategy and technical implementation details.

Career growth

Data careers grow toward senior analyst, staff data scientist, or data engineering lead. Many professionals specialize in machine learning, analytics, or infrastructure. Cross-functional work with product and engineering teams becomes more important at senior levels. The field changes quickly, so continuous learning is part of the job. Professionals who can translate numbers into decisions tend to advance fastest.