About Orakl Oncology

At Orakl Oncology, we are accelerating the development of oncology treatments. Today, fewer than 5% of new cancer drugs succeed in clinical trials. Clearly, new methods are needed. We combine cutting-edge biology and AI to build the next generation of insight platforms with the world's largest cohort of patient tumor avatars. These avatars fuel our AI-powered predictive engine, helping to anticipate clinical trial outcomes, validate therapies, and uncover new drug candidates.

Our mission is simple yet ambitious: to bring more effective treatments to patients who need them and to make drug development smarter, faster, and more personalised. We collaborate with top hospitals, research institutes, and pharmaceutical companies worldwide. Backed by leading investors, we are a fast-growing, mission-driven startup at the intersection of science and technology.

About the Role

Improving the quantity and quality of biological data is one of the most challenging bottlenecks to accelerate the rate of scientific discoveries in biology. In our labs at Orakl Oncology, we grow cancer cells from real patients, at scale, to generate our own experimental data. This data is deeply multi-modal and ranges from microscopic images, experimental metadata, to viability data and beyond.

We are looking for a Senior Data Scientist to own the wet-lab data chain end-to-end: from raw instrument and screening outputs through to processing pipelines. You will work on the canonical data model for lab data, as well as building the validated, analysis-ready datasets and tools our scientists and downstream teams rely on. This is a high-ownership, hands-on role at the intersection of data science, production engineering, and experimental biology. You will ship and maintain code in production, define how we model experimental data, and build the quality controls and feedback loops that let the lab catch errors early and iterate faster.

This is a role where you are expected to be in the lab, talk to scientists and lab technicians on a day-to-day basis. You will see how science is done in the lab, and then think hard about how to improve its impact. If you’re excited by complex workflows and the friction of real-life lab work, as well the power of data and AI, this position is for you.

What You'll Do

• Own the lab-based data generation pipelines: to enable data transfer at scale from the lab to the cloud. This will involve building and improving production codebases that turn high-throughput experimental readouts into validated insights.

• Facilitate lab operations : Maintain and/or build tools to accelerate the work of Orakl Oncology scientists.

• Define and own the canonical data model: Design the unified data model that structures our data and is adopted across teams, ensuring data can be joined across modalities: experimental, clinical, omics, and clinical-trial event reporting.

• Design and own data quality pipeline: Build QC that flags data quality at transfer time, and turn it into feedback loops so scientists can spot experimental errors early and integrate checks into their routine.

• Support lab initiatives: such as automation initiatives, the development of experimental tooling to help lab scientists and technicians.

• Drive operational efficiency: make sure that wet lab work is optimized to benefit the most from our resources (human and experimental) and ensure efficient contract and research delivery.

Minimum Qualifications

• Masters or PhD degree in Computer Science, Mathematics, Engineering, Bioengineering, or a related quantitative field.

• 3+ years of experience as a data scientist, with a proven track record of shipping and maintaining Python code in production

• Strong hacker and tinkerer mindset. You like building things that have a real world effects.

• Excellent proficiency in Python and SQL, and solid data-engineering skills: pipeline design and orchestration (Airflow, Dagster, dbt, or equivalent), version control (Git), testing/CI, and observability.

• Familiarity with cloud infrastructure (AWS, GCP, or Azure) and data-storage best practices.

• Comfortable working with noisy, real-world experimental data and directly alongside non-technical scientists.

Preferred Qualifications

• Hands-on experience with experimental, assay, or high-throughput screening data.

• Working knowledge of biology, ideally in oncology, cell biology, or drug discovery.

• Experience with image analysis / computer vision applied to microscopy data.

• Familiarity with lab data systems / LIMS (e.g. Benchling) and experimental workflows.

Who You Are

• Curious and autonomous: you dig into scientists' bottlenecks, connect the dots across teams, and ship without waiting for perfect specifications.

• A fast shipper: you get working solutions into production and iterate, rather than polishing in isolation.

• A strong communicator: you gather needs from and explain your work to non-technical scientists, acting as the data bridge across the lab and beyond.

• At ease in a complex and interdisciplinary environment: you thrive with messy data, evolving priorities, and external constraints that come with a science-first, non-pure-tech setting.

Why Join Orakl

• Own the wet-lab data backbone of a techbio platform from the ground up: your work directly shapes how we discover and validate cancer therapies.

• Sit at one of the most interdisciplinary intersections in techbio: data science, production engineering, and experimental oncology, side-by-side every day.

• High autonomy and immediately visible impact, in a fast-growing, mission-driven company backed by leading investors.

Interview Process

1. HR Call: Getting to know each other, aligning on expectations and context.

2. Technical Deep Dive: A deep conversation on your experience with production data pipelines, data modeling, and scientific data processing.

3. Technical Case: A system-design and problem-solving exercise representative of the real wet-lab data challenges you'll face at Orakl.

4. Reference Call: A conversation with one or two people you've worked with closely.

5. Founder Interview: A final discussion with our founders on vision, culture fit, and mutual ambitions.