Senior Machine Learning Engineer (AdTech)
Posted Updated
Description
- 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)