Application Guide

How to Apply for Data Scientist

at Highlightta

🏢 About Highlightta

Highlightta is a remote-first Canadian company focused on delivering data-driven insights through advanced analytics and machine learning. Their emphasis on integrating product data with Salesforce ecosystems makes them a unique player in the data science space, offering opportunities to work on end-to-end ML pipelines and collaborate across engineering and business teams.

About This Role

As a Data Scientist at Highlightta, you will own the design and deployment of machine learning models in production, leveraging MLOps practices. You'll also build data models and infrastructure to power customer-facing dashboards, directly impacting how clients derive value from their data. This role bridges data engineering, analytics, and ML, making it highly impactful for the company's product evolution.

💡 A Day in the Life

A typical day might involve collaborating with data engineers to review pipeline changes, then building and validating a new feature set for a customer-facing dashboard. You'll also spend time training and deploying an ML model using SageMaker, followed by monitoring its performance and iterating based on feedback from stakeholders.

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Interview Prep

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🎯 Who Highlightta Is Looking For

  • Has 4+ years of hands-on experience deploying ML models in production, not just in research or prototyping.
  • Is deeply proficient in Python for data manipulation and ETL/ELT, with a strong grasp of libraries like pandas, NumPy, and scikit-learn.
  • Possesses advanced SQL skills and expertise in dimensional modeling, data warehousing, and schema design for analytics.
  • Has experience with AWS SageMaker or similar cloud ML platforms, and is comfortable with automated deployment workflows (CI/CD for ML).

📝 Tips for Applying to Highlightta

1

Tailor your resume to highlight production ML deployments—include specific metrics (e.g., model latency, accuracy improvements, business impact).

2

Showcase your experience with data modeling and warehousing: mention star schemas, fact/dimension tables, or specific projects where you designed analytical data layers.

3

If you have experience integrating with Salesforce or CRM data, highlight it explicitly—it's a key collaboration point in the job description.

4

Prepare a portfolio or case study of an end-to-end ML project, from data collection to deployment and monitoring, emphasizing MLOps practices.

5

In your cover letter, mention specific AWS SageMaker features you've used (e.g., hyperparameter tuning, model registry) to demonstrate hands-on cloud ML experience.

✉️ What to Emphasize in Your Cover Letter

['Emphasize your experience deploying ML models in production and the business value they drove.', "Highlight your proficiency in data modeling and warehousing, especially if you've designed schemas for analytics or dashboards.", 'Mention any collaboration with data engineers or Salesforce architects to ensure data pipeline integrity.', 'Express enthusiasm for remote work and a Canadian company, showing you understand the cultural and time-zone considerations.']

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🔍 Research Before Applying

To stand out, make sure you've researched:

  • → Explore Highlightta's product offerings and blog to understand their data platform and how they deliver insights to customers.
  • → Research their tech stack—especially their use of AWS and any Salesforce integration patterns.
  • → Look at their team page on LinkedIn to understand the background of current data scientists and engineers.
  • → Check for any recent press releases or case studies about their ML or analytics capabilities.

💬 Prepare for These Interview Topics

Based on this role, you may be asked about:

1 Walk through a production ML project: data collection, feature engineering, model selection, deployment, and monitoring.
2 Design a dimensional model for a hypothetical product analytics dataset—explain your choice of facts and dimensions.
3 How would you ensure data quality and pipeline integrity when integrating data from multiple sources (e.g., product logs, Salesforce)?
4 Describe your experience with AWS SageMaker: which features did you use, and how did you automate the ML workflow?
5 How do you approach model versioning, A/B testing, and rollback in production?
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⚠️ Common Mistakes to Avoid

  • Don't focus solely on academic research or modeling without showing production deployment experience—Highlightta wants results in production.
  • Avoid generic statements about 'big data' or 'deep learning' without relevance to the role's focus on data modeling and MLOps.
  • Don't neglect the data engineering aspect: failing to mention SQL, ETL, or data warehousing could make you seem like a poor fit.

📅 Application Timeline

This position is open until filled. However, we recommend applying as soon as possible as roles at mission-driven organizations tend to fill quickly.

Typical hiring timeline:

1

Application Review

1-2 weeks

2

Initial Screening

Phone call or written assessment

3

Interviews

1-2 rounds, usually virtual

✓

Offer

Congratulations!

Ready to Apply?

Good luck with your application to Highlightta!

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