Lead Data Scientist

Insight Global; Burnaby; Remote; CAD 80,000 - 120,000; Full time

Job summary

An innovative AAA gaming company is on the lookout for a Lead Data Scientist to join their dynamic team remotely in Canada. This role is pivotal in supporting major mobile game titles, driving engagement through advanced data analytics. With a balanced focus on technical and non-technical aspects, the ideal candidate will thrive in an agile environment, leveraging their expertise in machine learning and statistical modeling. This position offers a unique opportunity to make a significant impact in a fast-paced industry, where creativity and analytical skills are highly valued. Join a diverse team committed to inclusivity and authenticity, and help shape the future of gaming through data-driven insights.

Qualifications

• 5+ years of experience in relevant fields or 3+ years with a Master's degree.
• Experience deploying machine learning models in production environments.

Responsibilities

• Lead the Mobile Portfolio within the Data and Analytics team.
• Develop and deploy machine learning models to enhance game engagement.

Skills

Machine Learning

Statistical Modeling

Python

SQL

Data Wrangling

Feature Engineering

A/B Testing

Communication Skills

Education

Master’s degree in Statistics, Mathematics, Computer Science, Machine Learning, Finance, Economics

PhD in a relevant field

Tools

Airflow

Docker

Kubernetes

Job description

Job Description

Insight Global is seeking a Lead Data Scientist for a large AAA gaming company remotely in Canada, on a 6-month contract-to-hire basis. As the leader of the Mobile Portfolio within the Data and Analytics team, you will support major mobile game titles in a high-profile role, innovating ways to increase game engagement. The team is dynamic, passionate, and fast-paced, requiring a Lead Data Scientist who is adaptable, driven to create value, and capable of working in an agile environment. The role is approximately 70% technical and 30% non-technical, involving communication with product stakeholders to gather requirements, prioritize, and execute tasks. Ideal candidates are excellent communicators, curious, self-motivated, and eager to make an immediate impact.

Technical responsibilities include developing and deploying supervised and unsupervised machine learning models such as classification and regression, with experience in production environments. You will also work on experimental design and statistical modeling, handling large datasets with proficiency. Experience with implementing machine learning models into live products, understanding both successful and unsuccessful deployments, is highly desirable.

We value diversity and are committed to creating inclusive environments where everyone can be authentic. We are an equal opportunity employer, considering candidates regardless of race, religion, sex, age, marital status, national origin, sexual orientation, disability, or other protected characteristics. For accommodations during the application process, contact [e-mail]. Additional information about privacy and rights can be found in our Workforce Privacy Policy and EEOC resources.

Skills and Requirements

1. 5+ years of relevant professional experience, or 3+ years with a Master’s degree in Statistics, Mathematics, Computer Science, Machine Learning, Finance, Economics, or a related quantitative field.
2. Experience with supervised/unsupervised machine learning, including deploying models into production environments (e.g., airflow, Docker, Kubernetes).
3. Expertise in experimental design and statistical modeling, including A/B testing, and familiarity with treatment/control setups and statistical power analysis.
4. Proficiency with large datasets, data wrangling, and feature engineering.
5. Proficient in SQL and Python.
6. Excellent communication skills, self-starter attitude, thought leadership, and stakeholder management capabilities.
7. Gaming industry experience is a plus.
8. PhD in a relevant field is preferred.
9. Experience with neural networks/deep learning models and causal inference experiments is advantageous.