Data Manager

About Us

At Matterworks we are building AI tools to extract insights from the ever-growing corpora of biological data and to unlock opportunities in therapeutic discovery, development, and manufacturing. We are building large-scale deep learning models of biological data to predict the phenotype and behavior of biological systems.

Position Overview

Matterworks is seeking a Data Manager (Bioinformatics / Cheminformatics) to build our data management practice, owning the strategy, processes, and day-to-day execution that turn complex, messy chemical and biological datasets into high-quality, well-governed training corpora and product-ready data assets.

You’ll be the connective tissue between applied science, AI, product, and our data platform/engineering team helping to answer what data is most valuable, how do we onboard it quickly, and how do we keep it consistently high quality over time.

This role will start as an individual contributor with end-to-end ownership, with a growth path to leading a function as we scale.

Key Responsibilities

• Data Strategy & E2E Ownership: Partner with scientific, ML, and product stakeholders to define a data roadmap: which datasets move the needle, which should be refreshed, and what “good enough” looks like for each use case. Establish clear success metrics for onboarding speed, dataset quality, and downstream usability (e.g., fewer training/data failures, higher match rates, better coverage, higher-confidence labels).

• Dataset Sourcing, Discovery, and Intake: Proactively scout and integrate public and client datasets, plus relevant literature and reference materials, to keep our corpora current and comprehensive. Design a repeatable dataset intake workflow including provenance, source tracking, and refresh cadence.

• Data Curation, Quality and Governance: Define curation standards that make data consistent across sources and modalities, including compound identity management, biological/sample metadata standardization, and schema + conventions mappings. Build a scalable approach to integrating metabolomics now and expanding to additional omics without reinventing everything each time. Develop practical QC/QA frameworks that combine scientific judgment with repeatable checks.

• Cross-Functional Collaboration: Work closely with leadership in engineering, AI, product, and scientific discovery to align initiatives with company-wide goals. Use experience to keep initiatives moving smoothly. Translate ambiguous questions into crisp data requirements, priorities, and execution plans. Build trust across disciplines by being both scientifically rigorous and pragmatically execution oriented.

About You

• 6+ years of demonstrated experience owning scientific data work end-to-end (curation, standardization, QC, documentation, governance) in bioinformatics, cheminformatics, computational biology, scientific data engineering, or related roles.

• Ability to navigate complex chemical and biological datasets, reconcile identifiers/metadata across sources, and make data consistently usable for end users.

• Strong attention to detail with a keen ability to balance priorities and delivery incremental value while operating with minimal oversight.

• Comfortable building structure from scratch: you can define processes, set standards, and iterate toward scalable practices in an early-stage environment.

• Practical proficiency in Python and SQL for data investigation, transformation, QC, and automation.

• Familiarity with modern data workflows (structured + semi-structured data, pipelines, reproducibility, documentation).

• Experience with chemical structure representations and normalization (e.g., SMILES/InChI, canonicalization, salt/tautomer handling, stereo chemistry considerations).

• Demonstrated ability to communicate and collaborate with product, machine learning, applied science and engineers while reducing complex business questions into valuable, reliable technical solutions.

• A passion for contributing to an early-stage startup where autonomy, eagerness to learn, and enthusiasm for solving novel scientific challenges prevail over rigid processes and egos.

Given the cross-disciplinary and…