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Added 12 mins ago
Product Manager, Accountant
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Full-time
A growing, technology-enabled professional services organization is seeking an accomplished Product Manager, Accountant to join its Toronto team. This is a newly created opportunity for a…
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Amaris Consulting

Data Science Specialist

Toronto, ON, Canada

Job type: Full-time
Apply by: Oct 23, 2026
Experience: 4–6 years of experience in Data Science, Data Analytics, Data Engineering, or related fields.
Posting language: English
Posted: Sep 23, 2026

Apply About employer

Applications open in a new tab.

Original job description

We are looking for a Data Science Specialist to support the development of advanced data and analytics solutions within a highly regulated manufacturing environment. The successful candidate will work closely with cross-functional teams to transform complex process and operational data into actionable insights, enabling data-driven decision-making and continuous improvement initiatives.

Location: Toronto, ON

Work Mode: Hybrid

Schedule: Flexibility to work with European time zones, including availability for an early shift (approximately 6:00 AM – 2:00 PM)

Key Responsibilities

Design, develop, and maintain ready-to-use data and analytics systems.
Extract, organize, contextualize, and transform data from multiple source systems into business-ready formats.
Collaborate with stakeholders across Manufacturing Science & Technology (MSAT), Automation, Digital, Quality, and other functions to gather analytics requirements.
Map process parameters and manufacturing data to source systems such as MES, PI Historian, LIMS, iShift, and cloud-based data platforms.
Develop data contextualization pipelines within cloud-based AI and analytics environments.
Build and maintain data visualization solutions and dashboards to support operational and business needs.
Develop and support machine learning models and advanced analytics solutions.
Ensure data integrity, consistency, and accessibility across multiple platforms.
Support continuous improvement initiatives through data-driven insights and process monitoring.

Required Qualifications

4–6 years of experience in Data Science, Data Analytics, Data Engineering, or related fields.
Strong experience working with manufacturing, process, or operational data.

Hands-on experience with:

Python
SQL (MS SQL Server)
Power BI
Snowflake
GitHub
Experience working with MES (Manufacturing Execution Systems), LIMS (Laboratory Information Management Systems), and process data platforms.
Knowledge of data modeling, data pipelines, data contextualization, and analytics solution development.
Strong communication skills and ability to collaborate with cross-functional stakeholders.
Ability to work in a fast-paced environment and manage multiple priorities.

Nice-to-Have Qualifications

Experience with PI Historian and PI Vision.
Experience with JMP, SIMCA, and SIMCA-online.
Exposure to Machine Learning and AI platforms.
Experience within Life Sciences, Pharmaceutical, Biotechnology, or other regulated industries.
Understanding of manufacturing processes and process performance monitoring.

Expected Compensation Range:

50K-100K The final compensation offered will be determined based on the candidate’s level of experience, skills, and qualifications, in compliance with applicable pay transparency requirements.

Canadian Experience Requirement:

No Canadian work experience is required for this position. Candidates are encouraged to apply regardless of previous Canadian experience.

Artificial Intelligence (AI) Disclosure:

No Artificial Intelligence (AI) tools are used in the screening, assessment, or selection of candidates for this position. All stages of the recruitment process are conducted by human recruiters and hiring managers.

Statement Regarding Job Vacancy:

This position is a Job creation, created to support ongoing project needs.

Recruitment Process & Feedback Timeline:

Candidates will receive feedback on their application within a maximum of 45 days from the date of application or from their most recent interview stage.

Why choose us

An international community bringing together more than 110 different nationalities
An environment where trust is central: 70% of our leaders started their careers at the entry level
A strong training system with our internal Academy and more than 250 modules available
A dynamic work environment that frequently comes together for internal events (afterworks, team buildings, etc.)

Amaris Consulting promotes equal opportunities. We are committed to bringing together people from diverse backgrounds and creating an inclusive work environment. In this regard, we welcome applications from all qualified individuals, regardless of sex, sexual orientation, race, ethnicity, beliefs, age, marital status, disability, or other characteristics.

Apply About employer

More about this job

Responsibilities

Develop and maintain analytics systems, data pipelines, dashboards, and machine learning solutions using manufacturing and operational data from multiple platforms. Collaborate with cross-functional teams to contextualize data, ensure its integrity and accessibility, and support process monitoring and continuous improvement.

Requirements

Requires 4–6 years of experience in data science, analytics, engineering, or a related field, with strong experience in manufacturing, process, or operational data. Candidates should have hands-on experience with Python, SQL, MS SQL Server, Power BI, Snowflake, GitHub, MES, and LIMS, along with data modeling and pipeline knowledge and strong communication and collaboration skills.

Skills

Data Science
Data Analytics
Data Engineering
Python
SQL
MS SQL Server
Power BI
Snowflake
GitHub
Manufacturing Execution Systems (MES)
Data Modeling
Data Pipelines
Data Contextualization
Machine Learning
Stakeholder Collaboration

Visa sponsorship

Not detected in the job text

Keywords

Data Science
Data Analytics
Data Engineering
Python
SQL
MS SQL Server
Power BI
Snowflake
GitHub
Manufacturing Execution Systems (MES)
PI Historian
PI Vision
iShift
JMP
SIMCA
SIMCA-online
Machine Learning
Artificial Intelligence
Cloud-Based Analytics
Data Modeling
Data Pipelines
Data Contextualization
Data Visualization
Dashboards
Manufacturing Science & Technology (MSAT)
Process Monitoring
Regulated Manufacturing
Life Sciences
Pharmaceutical

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