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Biotech Data Science Jobs in Ontario (NOW HIRING)

Premier Research

... Data Science, or a related quantitative field (Master's preferred). * 5+ yearsof programming experience in the pharmaceutical, biotechnology or CRO industry * 3+ years of hands-on experience with R ...

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Clarivate

Healthcare Research & Data Analyst

Toronto, ON · Hybrid

CA$54K - CA$68K/yr

... biotechnology and MedTech companies. We combine deep domain expertise and data driven analysis to ... Bachelor's degree with a concentration in sciences, healthcare, data analytics, or business.

Manifold Bio

Manifold Bio is a platform biotechnology company pioneering AI-guided protein design and massively ... Experience working with protein structure data (PDB, mmCIF) and/or protein sequence datasets

Next Generation

Process Engineer - III

Dublin, ON · On-site

$29.44 - $52.05/hr

Degree in Chemical Engineering, Biotechnology, Biochemistry, Biology, or a related scientific ... Your data will never be shared outside our organization without your prior written consent.

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Biotech Data Science information

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$25K

$116K

$201K

How much do biotech data science jobs pay per year?

As of Jul 30, 2026, the average yearly pay for biotech data science in Ontario is $115,986.00, according to ZipRecruiter salary data. Most workers in this role earn between $77,500.00 and $146,000.00 per year, depending on experience, location, and employer.

Can data scientists make $300k?

Biotech data scientists can potentially earn $300,000 or more annually, especially with extensive experience, advanced skills in machine learning and bioinformatics, and working in senior or specialized roles. Compensation varies based on location, company size, and individual expertise, with some senior-level positions reaching or exceeding this salary level.

What are the most common challenges faced by professionals in Biotech Data Science roles?

One of the primary challenges in Biotech Data Science is working with large, complex, and sometimes incomplete biological datasets, which require advanced analytical approaches and careful data curation. Professionals often need to stay current with rapidly evolving technologies and methods, which can be demanding but also rewarding for those who enjoy continuous learning. Collaboration with scientists, engineers, and regulatory teams is common, so adapting communication styles and translating technical findings to diverse audiences is key. Overcoming these challenges leads to meaningful scientific discoveries and significant career growth opportunities.

What are the key skills and qualifications needed to thrive in the Biotech Data Science position, and why are they important?

To thrive in Biotech Data Science, you need a solid background in biology or biotechnology, strong statistical and analytical skills, and experience with data analysis languages like Python or R. Familiarity with bioinformatics tools, sequencing platforms, and data visualization software is often expected, with certifications in data science or related fields considered a plus. Excellent problem-solving, communication, and collaboration skills are essential when working across multidisciplinary teams. These competencies enable effective interpretation of complex biological data, driving innovation and insights in the biotech industry.

What is a biotech data scientist?

A biotech data scientist analyzes biological and medical data to support research and development in the biotechnology industry. They use skills in statistics, programming, and machine learning, often working with tools like Python, R, and SQL to interpret complex datasets and inform decision-making.

What is a Biotech Data Science job?

A Biotech Data Science job involves analyzing complex biological and pharmaceutical data to drive research, innovation, and decision-making. Professionals in this field use machine learning, statistical modeling, and bioinformatics tools to extract insights from genomics, clinical trials, and drug discovery datasets. They collaborate with scientists, engineers, and healthcare professionals to improve treatments, develop new therapies, and optimize bioprocesses. Strong programming skills, domain knowledge in biology or biotechnology, and expertise in data analysis are essential for success in this role.

How can data science be used in biotechnology?

Biotech data scientists analyze large biological datasets to identify patterns, develop predictive models, and optimize processes such as drug discovery, genetic research, and personalized medicine. They use tools like machine learning, statistical analysis, and bioinformatics software to support research and development efforts in biotechnology companies and labs.

Can a biotechnologist become a data scientist?

A biotechnologist can become a data scientist by acquiring skills in programming, statistics, and machine learning, often through additional training or education such as online courses or advanced degrees. Their background in biology and laboratory data can provide a strong foundation for analyzing complex datasets in data science roles within biotech and healthcare industries.
What are the most commonly searched types of Biotech Data Science jobs in Ontario? The most popular types of Biotech Data Science jobs in Ontario are:
What are popular job titles related to Biotech Data Science jobs in Ontario? For Biotech Data Science jobs in Ontario, the most frequently searched job titles are:
What job categories do people searching Biotech Data Science jobs in Ontario look for? The top searched job categories for Biotech Data Science jobs in Ontario are:
Biotech Data Science jobs near you
Infographic showing various Biotech Data Science job openings in Ontario as of July 2026, with employment types broken down into 92% Full Time, 1% Part Time, 1% Temporary, and 6% Contract. Highlights an 81% Physical, 3% Hybrid, and 16% Remote job distribution, with an average salary of $115,986 per year, or $55.8 per hour.

Principal Data Scientist (Founding)

Katalyze AI, Inc.

Toronto, ON • On-site

$120 - $180/hr

Other

Posted 6 days ago

Job description

About Katalyze AI

Katalyze AI is a fast-growing AI-driven biotech platform company on a mission to make life-saving drugs accessible and affordable for everyone. Our AI Agents help pharmaceutical and biotech companies increase production efficiency, reduce costs, and minimize waste. We're a team of humble, fast-moving, and curious craftspeople working at the intersection of science and AI.

About the Role

We're looking for a Principal Data Scientist to join Katalyze AI and work at the intersection of applied statistics, machine learning, and biotechnology. You'll independently analyze complex scientific and process data build interpretable predictive models, and translate findings into actionable recommendations for enterprise customers in biopharma and advanced manufacturing.

This is a high-ownership, customer-facing role. You'll work directly with scientists and engineers at our accounts, not just hand off reports internally.

What You'll Do

Build predictive and diagnostic models on scientific and industrial data (time series, multivariate sensor data, spectral data, batch records)

Select and apply the right modelling technique for each problem — gradient-boosted trees, Gaussian processes, neural networks, classical statistical models — with clear reasoning for your choices

Apply signal processing and time series methods (Fourier transforms, wavelet analysis, autocorrelation, decomposition, forecasting) to real-world sensor and process data

Design rigorous model evaluation frameworks: cross-validation strategies for time-series data, SHAP-based interpretability, uncertainty quantification, and statistical significance testing

Build interpretable ML pipelines that surface drivers of variability in ways that satisfy audit and documentation requirements

Design analytics dashboards that communicate complex statistical findings to manufacturing scientists, quality teams, and supply chain managers

Work closely with the Deployment Strategist to configure and deliver data science components for customer deployments

Partner directly with enterprise customers to understand their data challenges, deviation patterns, and quality systems

Apply LLM-based approaches where appropriate to automate insight generation and multi-step analytical workflows

What We're Looking For

6+ years of applied data science experience with a strong foundation in statistics and machine learning

Deep understanding of how model families work — linear/logistic regression, tree-based models (XGBoost, LightGBM, CatBoost), SVMs, neural networks, transformers — and when to use each

Strong time series expertise: Fourier analysis, wavelet transforms, autocorrelation, stationarity, decomposition, and forecasting (ARIMA, Prophet, and deep learning approaches)

Rigorous model evaluation skills: proper train/test design for time-series data, overfitting detection, SHAP and interpretability methods, uncertainty quantification

Experience with Gaussian processes, Bayesian methods, or uncertainty quantification

Strong Python skills: scikit-learn, pandas, numpy, statsmodels, PyTorch or TensorFlow

Experience with enterprise data infrastructure — SQL, data warehouses, cloud platforms (Snowflake, Databricks, Redshift)

Strong communication skills — able to explain statistical findings clearly to scientists, engineers, and business stakeholders

Experience with scientific or industrial data (sensor streams, spectral data, batch records, LIMS/MES outputs) is a strong plus

PhD or Master's in Data Science, Statistics, Chemical Engineering, or related field preferred

Experience with LLMs or agentic systems is a plus, not a core requirement

ML & Analytics: Python, scikit-learn, XGBoost, LightGBM, PyTorch, statsmodels

LLM / Agents: Claude/GPT APIs, LangChain (where applicable)

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