Who you are:

We are always looking for amazing talent who can contribute to our growth and deliver results! Geotab is seeking an Insights & Integrations Consultancy Data Scientist who will be responsible for transforming complex data into clear, business-impactful insights that drive portfolio expansion, retention, and real-world outcomes for fleet operators around the world. This role sits at the intersection of data science, customer consultancy, and sustainability — where your ability to communicate the why behind the data matters as much as the analysis itself. If you love turning data into decisions and have a passion for sustainability and clean transportation — we would love to hear from you!

What you'll do:

As an Insights & Integrations Consultancy Data Scientist, your key area of responsibility will be partnering with strategic accounts to drive ecosystem expansion and retention — not just through technical analysis, but by translating that analysis into business impact that resonates with fleet managers, executives, and non-technical stakeholders. You will develop and maintain deep expertise across the Geotab platform, engaging directly with partners and end-users to optimize their use of Geotab's technology, presenting data-driven insights in ways that clearly demonstrate ROI, cost savings, and efficiency gains. Day-to-day, you will design and deliver Big Data dashboards and analytics tools, maintain Airflow pipelines for continual data analysis using SQL and Python, develop proof of concepts, and solve complex business problems through customer-tailored algorithms and predictive models. You will also represent Geotab as a thought leader at industry events and trade shows — particularly in the areas of fleet electrification and sustainability — and travel quarterly to Canada for team meetings, with additional on-sites at key strategic accounts.

You will work closely with Account and Engagement Managers within Geotab's Sales Channels, the Signature Accounts Team, Product Management, and Solutions Engineering — relationships that are essential to identifying growth opportunities and translating customer feedback into product improvements. To thrive in this role, you will bring an exceptional ability to communicate complex technical concepts to non-technical audiences, a consultative mindset focused on business outcomes over technical jargon, and a genuine passion for sustainability and the electrification of commercial fleets.

How you'll make an impact:

• Develop and maintain comprehensive knowledge of the Geotab ecosystem, serving as a Subject Matter Expert (SME) to empower end-users and partners and enhance their understanding of Geotab solutions.

• Engage and collaborate with partners and end-users to optimize their utilization of Geotab's technology, products, and solutions, tailoring the approach to meet their specific needs.

• Contribute to the continuous improvement of the company's offerings by providing partner and end-user feedback, providing opportunities to collaborate with Product Management teams.

• Create and optimize data-driven dashboards for cross-functional use, facilitating informed decision-making and efficient collaboration with a focus on expansion and retention of existing customers.

• Ensure partner and end-user satisfaction through prompt and accurate service, bolstering internal team support by providing additional insights and know-how.

• Collaborate with the Geotab revenue generating teams to identify and deliver effective solutions for customer challenges, by working closely with them and their partners, contributing to sales growth, ecosystem expansion and customer retention.

• Support Geotab's partner upskilling, driving them towards excellence to support customers across unique channels, using best practices, and leveraging a deep understanding of industry verticals.

• May be involved in developing and implementing proof of concepts. This involves creating a small-scale version of the solution to demonstrate its feasibility and effectiveness in the customer's environment.

• Staying updated with industry trends, competitor products, and market demands is crucial. Need to understand the broader landscape in which Geotab operates to position their solutions effectively.

• Keep accurate records of customer interactions, solution designs, and outcomes is important. This helps in creating case studies, improving processes, and maintaining a repository of successful solutions for reference.

• Deliver compelling technical presentations and demonstrations to customers. They showcase the features and benefits of the product or service, highlighting how it solves the customer's problems.

• Work closely with the sales team and fellow engineers throughout the sales cycle as needed. Provide technical support, answer technical questions, and address concerns that potential customers may have in regards to their area of subject matter expertise.

• Based on customer requirements, design customized solutions or configurations using the company's products. They create proposals and presentations that outline how the solution meets the customer's needs, often through demonstrations or prototypes.

• Even before a sale is made, may assist with technical inquiries from potential customers. This could involve explaining technical details, providing documentation, or guiding customers on how to integrate the solution into their existing systems when expertise beyond the general area is required.

• Solve complex business problems by providing analytics or analytics-based consulting, including customer-tailored algorithms and predictive models, under guidance or mentorship.

• Maintain airflow pipelines for continual data analysis combining SQL queries and python scripts.

• Design, maintain and deliver Big Data dashboards and tools; document stakeholder requirements, resolve issues, and fulfill change requests.

What you'll bring to this role:

• Bachelor's degree in Engineering, Computer Science, Information Technology, or a related field; equivalent combination of education and/or work experience may be substituted.

• 3–5 years of experience in a relevant customer-facing technical role, including Solutions Engineering, Sales Engineering, Integration Engineering, Engineering Support, Software Development, Data Science, or Technical Account Management.

• 1–3 years of hands-on experience with SQL (Google BigQuery preferred) and data science tools (Python, pandas, numpy preferred).

• Experience working with large data environments and demonstrated ability to extract value from very large datasets, including data modeling and data warehouse concepts.

• Data visualization skills, particularly in dashboarding platforms such as Power BI or Tableau.

• Technical knowledge of telematics, IoT, APIs, and related technologies; working knowledge of hardware integrations is a bonus.

• Strong communication and presentation skills, with the ability to articulate complex concepts to both technical and non-technical audiences, and to collaborate effectively across cross-functional teams.

• Effective problem-solving and critical thinking abilities, with the capacity to assess customer challenges and propose viable solutions.

• A customer-focused mindset with strong time management and organizational skills; able to manage multiple projects and priorities simultaneously.

• Desire to stay current with emerging technology and the flexibility to adapt to growing market demands.

• Ability to travel up to 20% of the time internationally; valid passport required.

Why job seekers choose Geotab:

Mission Summary:

At Motional, we're transforming how autonomous vehicles discover critical intelligence hidden within petabytes of multimodal sensor data. Our next-generation autonomous driving stack depends on finding the rare edge cases, long-tail scenarios, and model errors that matter most. Omnitag, our ML-powered multimodal data mining framework, is the engine that powers this discovery.

As a Machine Learning Engineer on the Data Mining team, your mission is to help build the "Brain" of this engine. You will work with state-of-the-art foundation models to extract insights from Motional’s driving data, working at the intersection of large-scale representation learning and data retrieval. By building smarter mining tools and efficient data pipelines, you will accelerate the model improvement lifecycle for teams working on post-training analysis, error diagnosis, and dataset curation.

What You'll Do:

• Build and Train ML Pipelines: Develop, train, and fine-tune machine learning models for multimodal sensor data (e.g., vision, LiDAR). Focus on implementing supervised and self-supervised learning approaches to improve data search and retrieval.
• Support Model Deployment: Implement scalable data preprocessing and augmentation pipelines. Assist in applying standard optimization techniques (e.g., batch inference, quantization) to ensure models run efficiently in production environments.
• Data Mining & Analysis: Help develop embedding-based search tools and "active learning" workflows to identify critical driving scenarios.
• Monitor Production Performance: Help build and maintain dashboards to monitor model health, data drift, and system performance. Identify regressions and assist in the operational support of our data mining services.
• Learn and Apply Best Practices: Follow software engineering standards (version control, CI/CD, unit testing) for ML code. Participate in code reviews and contribute to technical documentation.
• Collaborate Across Teams: Work closely with senior engineers and machine learning engineers to translate model prototypes into maintainable, scalable engineering solutions.
  
  

What We're Looking For (Must-Haves):

• BS or MS in Computer Science, Machine Learning, or a related field.
• Hands-on experience with PyTorch (preferred) or TensorFlow/JAX. You should be comfortable training models and evaluating them using standard metrics.
• Strong proficiency in Python with the ability to write clean, modular, and well-documented code.
• Working knowledge of version control, unit testing, and basic software design patterns.
• Experience working with large datasets, including proficiency in SQL and data libraries like Pandas and NumPy.
• A solid grasp of the full ML lifecycle, from data cleaning and feature engineering to validation and deployment basics.
• A proactive learner who thrives on constructive feedback and is eager to grow within a high-stakes engineering environment.
  
  

Bonus Points (Nice-to-Haves):

• MS/PhD in Computer Science, Machine Learning, or related field.
• Experience with agentic systems, autonomous reasoning, chain-of-thought models, or LLM-based planning.
• Background in autonomous driving, robotics, or real-time decision-making systems.
• Familiarity with multimodal learning, sensor fusion, or embodied AI.
• Experience building active learning loops, using the model to find the data that breaks the model.
• Experience with ML-based data mining, active learning, or contrastive learning.
• Knowledge of model serving tools (TF Serving, Triton, TorchServe) and MLOps platforms.
• Publication in top-tier conferences (e.g., ICCV, CVPR, ECCV)
  
  

We encourage a hybrid schedule with in-office time at one of our locations in Boston, Pittsburgh, or Las Vegas to support collaboration, or this role can be fully remote.