Blackstraw.ai is an end-to-end technology services company specializing in Artificial Intelligence (AI) and Engineering solutions across Data Science, Data Engineering, LLM/GenAI and LLMOps. Founded in 2018, we help global enterprises across North America, Europe and Asia to build and operationalize AI systems that create measurable business impact. Our mission is to make AI adoption simpler, faster and scalable through a blend of deep domain expertise, reusable accelerators and proven engineering practices.

With a 600+ strong team of engineers, data scientists and AI specialists, we partner with organizations to deliver real-world outcomes in areas such as predictive analytics, computer vision, natural language processing and Generative AI. Headquartered in Florida (USA) with operations in Canada and India, Blackstraw.ai continues to empower global enterprises to unlock the true potential of AI.

Location: Canada

Experience: 6 to 12 yrs

Role Summary: We are seeking an experienced Data Scientist with solid expertise in time‑series forecasting and operational analytics to support manufacturing performance optimization . In this role, you will build analytical frameworks, evaluate production capacity and throughput, and translate complex operational data into clear, actionable insights. You will work closely with plant, engineering, and analytics teams to validate assumptions, refine models, and drive data‑informed decision‑making across high‑volume production environments.

Core Responsibilities:

- Develop and continuously refine analytical frameworks to assess capacity, throughput, and operational constraints across all stages of potato fries production.
- Partner closely with plant operations, engineering teams, and insights stakeholders to validate assumptions, enhance models, and support data‑driven decision‑making.
- Convert complex operational and time‑series data into clear, actionable insights through high‑quality visualizations and storytelling.




- Build scalable tools and dashboards that track key performance metrics and empower teams with self‑serve analytics capabilities.

Key Skills & Qualifications

1. Technical & Analytical Proficiency

- Proficient in Python for data analysis, modeling, and building repeatable analytical workflows
- Skilled in wrangling, cleaning, and analyzing time-series and operational datasets to surface actionable insights.
- Working knowledge of statistical methods and foundational machine learning techniques to support data-driven decision‑making.
- Proficient in data visualization tools (e.g., Python and Power BI).
- Ability to translate data findings into clear, concise, and actionable visuals.
- Skilled in crafting presentations style story telling for both technical and non-technical audiences.

3. Manufacturing Domain Acumen

- Demonstrated interest in or experience with manufacturing, operations, or industrial systems (e.g., refrigeration, production lines, capacity planning).
- Ability to understand plant operations, process flows, and constraints to contextualize analysis.

4. Problem Solving & Initiative

- Comfortable navigating ambiguous problems and structuring them into clear, actionable analytical tasks.
- Track record of building analysis frameworks from the ground up or refining existing ones for greater impact.

5. Communication & Stakeholder Engagement

- Strong written and verbal communication skills.
- Ability to collaborate cross‑functionally with engineering, operations, and analytics teams.
- Comfortable presenting findings and recommendations to mid‑to‑senior level stakeholders.

Education

- Bachelor’s degree in Computer Science, Engineering, IT, or equivalent experience.

Blackstraw provides equal employment opportunities to applicants and employees without regard to race, color, religion, age, sex, sexual orientation, gender identity/expression, national origin, marital status, protected veteran status, disability status, or any other basis as protected by federal, state, or local law

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📌 Data Science – Time Series Forecasting (Toronto)
🏢 Blackstraw
📍 Toronto