Machine Learning Engineer

Morningstar, Inc.; Toronto; Hybrid; USD 96,000 - 164,000; Full time

Job summary

Morningstar is seeking an experienced Machine Learning Engineer to lead the ML function within the Marketing Intelligence Team. This role involves setting requirements for ML models, building data pipelines, and ensuring model outputs align with business goals. The candidate will need strong technical skills, and also the ability to collaborate with marketing stakeholders to drive successful outcomes, demonstrating their analytical prowess through problem-solving in complex data environments.

Benefits

Comprehensive health benefits including dental and vision

75% 401k match up to 7%

Trust-Based Time Off

6-Week Paid Sabbatical Program

Annual Education Stipend

Tuition Reimbursement

Qualifications

• 7+ years of experience in building data science models.
• Expertise in marketing analytics & experimentation.
• Strong background in big data processing.

Responsibilities

• Build, fine-tune, and implement machine learning models.
• Ensure scalable, repeatable machine learning production pipelines.
• Collect, clean, manage, and visualize large data sets.

Skills

Machine Learning

Data Engineering

Statistical Techniques

Data Visualization

Analytical Skills

Education

Master’s degree in Statistics, Finance, Mathematics, Engineering

Tools

Python

R

SQL

Apache Spark

TensorFlow

PyTorch

Scikit-learn

Job description

About the Role

We’re looking for a seasoned Machine Learning Engineer to lead the Machine Learning function within the Marketing Intelligence Team at Morningstar. This role will play a critical role by setting the requirements for the ML models,building the data pipelines and flows to power the model, as well as output model data that can be visualized and shared to business leaders. You will have deep technical data engineering skills and vision to build the machine learning road map. You will ensure models align to business goals, and are responsible forrefining methodologies, iterating on models, and fine-tuning the model output. You will share out model findings to business stakeholders and tune and monitor models.You will evangelize model findings and feature engineering insights to marketing stakeholders and facilitate opportunities for stakeholders to use model output to deliver stronger business outcomes.

To foster continuous collaboration, we follow a hybrid policy of a minimum of 3 days onsite in our Chicago office.

Responsibilities

• Build, fine-tune, and implement machine learning models to answer challenging business questions
• Ensure machine learning production pipelines are scalable, repeatable, and cloud agnostic
• Apply current and emerging techniques in deep learning, AI, and other machine learning areas
• Collect, clean, manage, analyze, and visualize large sets of data using multiple data platforms, tools, and techniques
• Optimize and fine-tune ML models for performance and scalability. Analyze and interpret data to extract meaningful insights and improve ML models
• Maintain a database of model outputs, which will be used by the rest of theMarketing Intelligence team
• Integrate ML solutions into Marketing Intelligence workstreams
• Document and present findings and solutions to leaders and stakeholders

Requirements

• Master’s degree preferred, ideally in statistics, finance, mathematics, engineering, analytics, or in a quantitative discipline
• 7+ years proven experience building data science models and executing data engineering work
• Extensive experience in machine learning and statistical techniques, including regression, classification, clustering, time series forecasting, text analytics, and causal inference, and scaling end to end solutions
• Expertise in marketing analytics & experimentation, such as Marketing Mix Modeling (MMM), Attribution Modeling, Lead Score Propensity Modeling
• Proficiency in Python, R, and SQL, with strong scripting capabilities to process and transform data for modeling
• Strong background in big data processing and engineering using Apache Spark, PySpark, and Airflow, with experience in data lakes and warehousing solutions (Snowflake, Databricks, Redshift, BigQuery)
• Experience with MLOps and cloud platforms (AWS SageMaker, Bedrock, Google Vertex AI, Azure ML), with a track record of automating machine learning pipelines and building end-to-end ML products
• Hands-on experience with machine learning libraries, including Scikit-learn, Pandas, NumPy, Matplotlib, SciPy, Seaborn, XGBoost
• Hands-on experience with deep learning frameworks (TensorFlow, PyTorch)
• Outstanding analytical and problem-solving skills with technical knowledge of setting requirements, working with complex data, and leveragingdata stored across multiple data environments
• The ability to solution alongside team members andthrive in a collaborative environment, and convey complex concepts in a clear and concise manner

Compensation and Benefits

At Morningstar we believe people are at their best when they are at their healthiest. That’s why we champion your wellness through a wide-range of programs that support all stages of your personal and professional life. Here are some examples of the offerings we provide:

• Financial Health
  • 75% 401k match up to 7%
  • Stock Ownership Potential
  • Company provided life insurance - 1x salary + commission

• Physical Health
  • Comprehensive health benefits (medical/dental/vision) including potential premium discounts and company-provided HSA contributions (up to $500-$2,000 annually) for specific plansand coverages
  • Additional medical Wellness Incentives - up to $300-$600 annual
  • Company-provided long- and short-term disabilityinsurance

• Emotional Health
  • Trust-Based Time Off
  • 6-week Paid Sabbatical Program
  • 6-Week Paid Family Caregiving Leave
  • Competitive 8-24 Week Paid Parental Bonding Leave
  • Adoption Assistance
  • Leadership Coaching & Formal MentorshipOpportunities
  • Annual Education Stipend
  • Tuition Reimbursement

• Social Health
  • Charitable Matching Gifts program
  • Dollars for Doers volunteer program
  • Paid volunteering days
  • 15+ Employee Resource & Affinity Groups

Base Salary Compensation Range

$96,326.00 - 163,761.00 USD Annual

Total Cash Compensation Range

$110,775.00 - 188,325.00 USD Annual

Morningstar’s hybrid work environment gives you the opportunity to work remotely and collaborate in-person each week. While some positions are available as fully remote, we’ve found that we’re at our best when we’re purposely together on a regular basis, typically three days each week. A range of other benefits are also available to enhance flexibility as needs change. No matter where you are, you’ll have tools and resources to engage meaningfully with your global colleagues.

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