“The front page of the internet," Reddit brings over 430 million people together each month through their common interests, inviting them to share, vote, comment, and create across thousands of communities. 

We are looking for a manager to be a part of the Ads Data Science team and lead a small group of machine learning data scientists. You will work closely with engineering managers, engineers and product owners from our Ads team to optimize ads delivery and auction systems. In addition to strong ML skills, this person has a solid business acumen and understands what is important to advertisers. 

Responsibilities:

• You will start off with leading a team of 4-5 ML data scientists with the expectation of scaling the team to 10+ in 2022.
• You will build optimization algorithms that improve ad yields and efficiencies. Our optimization models were hugely successful in the last 12 months and as a result we are increasing our investment in this area.
• Along with building ML models, you will be a key strategic player in defining the roadmap for the ML efforts for the entire Ads Org. Your roadmap will not only achieve immediate business goals, but also dictate our long term strategy for optimization and marketplace efforts.
• Manage and nurture a team of talented Data Scientists and have a keen interest in shaping their careers.
• Some of the models we developed over the last year are CPC (cost per click), CPI (cost per install), Generalized pCVR (probability of conversion rate), ALO (Ad Level Optimization) and User Lookalikes. You will not only work on improving existing optimization models, but also develop new models from scratch for a variety of upcoming product launches.
• Build and improve Machine Learning algorithms that match ads to the most relevant users. Some of the algorithms/techniques used are Logistic Regression, Gradient Boosted Decision Trees, Random Forests, Hyperparameter tuning, Thompson Sampling, Monte Carlo simulations, Semantic Embedding models etc.
• Design and build a platform for rapid model iteration and feature engineering at scale. Some of our optimization models are iterated on and deployed in production every 2 weeks!
• Be involved in all phases of modeling such as ideation, offline modeling, online implementation, experimentation, deploy and post-launch monitoring/measurements.
• This role will have a lot of overlap with other Machine Learning Engineer roles, but will differ in a couple of areas. First is that you will work mainly on offline modeling and rely on engineers to productionize your models. Secondly, you will have a keen interest in the collection and quality of underlying data, along with working on ETLs and data aggregations.
• Serve as a thought-partner for product managers, engineering managers and leadership in influencing the monetization roadmap and strategy for Reddit by identifying opportunities through deep-dive analyses and/or modeling.
• Work closely with our sales and marketing partners to ensure that ads are set up in a way that amplifies the benefits of your optimization models.

What We Can Expect From You:

• Master’s or PhD degree in a quantitative major (e.g., mathematics, statistics, economics, finance, computer science). 
• Proficiency in Machine Learning
• 2 years of Prior experience working as a Tech Lead or a Data Science Manager. Willing to consider candidates without prior management experience.
• 5+ years of experience in quantitative/modeling roles, preferably for a consumer-facing service/app
• Proficiency with statistical analysis and programming languages (Python, SQL)
• Understanding of experimentation and causal inference analyses
• Experience building Ads optimization models is preferred but not required