Lead Data Scientist

Title: Lead Data Scientist

Location: Remote Canada, prefer if physically in Vancouver (may consider US candidates)

Contract length: 6-month contract to hire – this long-term intention is a must!

Day to Day

Insight Global is looking for a Lead Data Scientist to join a large AAA gaming company remotely in Canada for a 6-month contract to hire opportunity. As a lead of the Mobile Portfolio in the Data and Analytics team, you will be supporting some of the largest mobile game titles in a high-profile role on a cutting-edge project to find mathematically vigorous new ways of getting games in hands. This team is fast-paced, and passion driven, and requires a Lead Data Scientist that is flexible to drive value, work in an agile environment, and who wants to make immediate impact. This role is 70% hands-on technical and 30% non-technical. Non-technical aspects include communicating with product stakeholders to acquire requirements and then prioritize and execute against them. Someone who is an excellent communicator, curious, self-driven and motivated will be a good fit. Areas of technical focus include working with supervised/unsupervised machine learning models such as classification and regression, and assisting getting models into production. Further, you will be working with data science experimental design and set up and statistical modelling on a day-to-day. Proficiency with huge data sets will be necessary prior experience. Overall, someone who has experience implementing machine learning models into products in a live environment, and preferably has seen both a successful and non-successful instance, will have transferable experience to be successful in this role.

Must haves

• 5+ years relevant professional experience, or 3+ years experience and a Masters in Statistics, Mathematics, Computer Science, Machine Learning, Finance, Economics, or another quantitative field
• Experience with supervised/unsupervised machine learning (ML) such as classification and regression models, including at least some experience getting models into production (e.g. airflow, docker, Kubernetes)
• Experience with data science experimental design and set up (e.g., treatment vs. control, statistical power) and statistical modelling (e.g., A/B testing, parametric and non-parametric tests, ANOVA)
• Proficiency with huge data sets, including data wrangling and feature engineering capabilities
• Proficient in SQL
• Proficient in Python
• Superior communication, self-starter, thought leader, strong stakeholder management capabilities

Plusses

• Gaming background
• PhD in Statistics, Mathematics, Computer Science, Machine Learning, Finance, Economics, or another quantitative field
• Experience with neural networks and/or deep learning ML models
• Experience with causal inference experimentation data science