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Day 76
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Toronto, Ontario, Canada
Live, first seen by us 76 days agoEmployment type in the employer’s posting: Full-TimeFrom the employer’s posting
B3 enables enterprise manufacturers to digitize their operations and use AI to improve performance.
In this role, you will own client engagements from end to end, starting from raw and often messy plant data and finishing with validated findings and working models.
Ingest and audit raw client plant data, then explore and engineer features
Build and validate models for anomaly detection, predictive maintenance and forecasting
Produce clear write-ups, presentations and validated numbers for client experts
Agree on success criteria with clients before each build starts
Bring proven models and tools into the company platform for multiple clients
B3 enables enterprise manufacturers to digitize their operations and use AI to improve performance. Our AI & Data Science team builds the models, monitors, and AI features behind the platform, and a growing share of our work comes from client engagements: taking a manufacturer's raw operational data and turning it into insights and models they can act on.
In this role, you will own client engagements from end to end, starting from raw and often messy plant data and finishing with validated findings and working models. The data is industrial; time-series, sensor readings, downtime events, and shift reports and the audience is demanding in the best way: operators and plant managers who know their numbers well and will verify yours. We hold our work to that standard.
ingesting raw client data, auditing its quality, exploring it, engineering features
Build models for industrial problems such as anomaly detection, early-warning and predictive-maintenance questions, and forecasting
Produce consulting-grade deliverables: clear write-ups and presentations, validated numbers
Agree on success criteria with the client before a build starts
Work with the team to bring proven models and tools into the platform
4+ years of experience in data science or machine learning, including end-to-end ownership of projects you personally carried from messy source data to a delivered outcome.
Experience with predictive maintenance, anomaly detection, or forecasting
Strong Python skills and experience with the standard stack (pandas, scikit-learn, PyTorch or TensorFlow).
Experience deploying models into production environments
Degree in Data Science, Computer Science, Engineering, Mathematics, Statistics, or a related field.
Familiarity with SQL and data pipelines
Experience with real-world, messy datasets; time-series, sensor, or event data is preferred.
Own client engagements end to end: ingesting raw client data, auditing its quality, exploring it, engineering features, building and validating models, and delivering the results.
In the employer’s words · Read in context
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Day 76
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From 11,862 similar postings that came down. Coming down is not always a hire.
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- Comfortable working with stakeholders when the work calls for it: presenting findings clearly and asking good questions of the people who know the data best. - Strong Python skills and experience with the standard stack (pandas, scikit-learn, PyTorch or TensorFlow). - A solid foundation in statistics, modeling, and model evaluation — you know when a result is real, and you can demonstrate why.
- Experience deploying models into production environments - Familiarity with SQL and data pipelines - Experience with Azure or cloud-based data platforms
- Familiarity with SQL and data pipelines - Experience with Azure or cloud-based data platforms - Understanding of industrial, manufacturing, or operational data
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New Data Scientist postings at the employers we follow are down 4% over the last 6 weeks compared with the 6 before.
Each bar is one week of new postings. The darkest bars are the last 6 weeks.
Counted from the hiring pages we’ve followed for at least five weeks, so employers we add don’t look like growth. Updated weekly.
We file this job under Data Scientist, by the work the posting describes.
Data scientists use statistics and models to answer harder questions, such as whether a change worked, what demand will look like next quarter, or which customers are likely to leave.
Postings usually ask for Python or R, SQL, statistics, experiment design and common machine learning methods.
Unlike an ML engineer, a data scientist's main output is often an analysis or a first model, not software running inside a product.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-2051.
Sr. Data Scientist
About the Role
B3 enables enterprise manufacturers to digitize their operations and use AI to improve performance. Our AI & Data Science team builds the models, monitors, and AI features behind the platform, and a growing share of our work comes from client engagements: taking a manufacturer's raw operational data and turning it into insights and models they can act on.
In this role, you will own client engagements from end to end, starting from raw and often messy plant data and finishing with validated findings and working models. The data is industrial; time-series, sensor readings, downtime events, and shift reports and the audience is demanding in the best way: operators and plant managers who know their numbers well and will verify yours. We hold our work to that standard.
You will take project scoping and client goal setting conversations, and can grow into leading them over time. You will turn a well-scoped engagement into results the client can trust. The strongest of your work will graduate into our platform, where it serves clients beyond the one it was built for.
About the Work
About You
Bonus Points
About the Office
If you’re driven by applied AI, enjoy working in the future, and want to build data science solutions that make a real impact, we’d love to meet you.
Note: This position reflects an existing vacancy. B3 Systems is proud to be an Equal Employment Opportunity employer. We truly appreciate your interest in joining our team. While we may not be able to connect with every applicant, we will reach out directly to those selected for next steps. No “artificial intelligence” tool will be used to screening, assessment or selection of applicants for this opportunity.
We celebrate diversity and are committed to creating an inclusive, supportive workplace where everyone can thrive. If you require any accommodations or support at any stage of the selection process, please let us know—we’re happy to help.
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Toronto, Ontario, Canada
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