Senior Machine Learning Engineer (AdTech)

• As a member of our Data Science Engineering team, the Sr. Machine Learning Engineer, AdTech will focus on optimizing real-time bidding strategies and auction mechanics to efficiently spend ad budgets and deliver against campaign targets
• Analyzing and optimizing real-time bidding strategies and online auction mechanics
• Developing new or improving existing models of event predictions
• New feature engineering for multiple machine learning models:
• User embeddings and clustering; fraud detection, etc
• Cross-device user identification, cookieless mechanisms development
• Mining different data sources
• Supporting existing codebase for data integration and production support for our core models

Benefits

• Dollars and Sense: 401(k) match
• Happy + Healthy: Comprehensive medical plans, affordable medical, dental and vision options, 100%-paid life & disability insurance
• Break a Sweat: Free virtual fitness classes, Better Yourself Wellness program
• Always Learning: Generous annual tuition reimbursement, ongoing team trainings
• Take a Load Off: Paid vacation, sick time, and company holidays (including a floating holiday)
• Good Ol’ Fun: Team-building events, happy hours, holiday celebrations, and more!- 5 years minimum of experience in machine learning/data science
• Algorithms and Data Structures (e.g., sorting, search tree, binary heap, trie; time & mem complexities of algorithms)
• Advanced knowledge of Python using standard DS packages (numpy/pandas/scikit, etc.); Being able to optimize and speed-up code
• Key Skills: Python, Algorithms, Optimisation, NLP, Data Mining, Statistical Analysis, Neural Networks, Generalised Linear Regression, Multiclass Classification, Java, R
• 3+ years of RTB Auction or similar online technologies
• Probability and Statistics (e.g., hypothesis testing; Markov process and its stationary distributions, stochastic matrix and its properties; Bayesian inference)
• ML & DS (e.g., dimensionality reduction, geometry of PCA / SVD and of L1 / L2 regularisation, Decision trees and their ensembles, collaborative filtering, Thompson sampling / MCMC, Neural Networks, etc.)- Initial Screening Call (30 mins)
• Technical Pre-Screening Call with Principal Data Scientist (60 mins)
• Team Interview (around 4-5 hours total)
• WebMD/IB Sr. Tech Leader (30 mins)