Machine Learning Engineer, AdTech
1 day ago
Description
Function: Engineering, R&D → Data Science / Machine Learning / Operations Research
About PulsePoint:
PulsePoint is a fast-growing healthcare technology company (with adtech roots) using real-time data to transform healthcare. We help brands and agencies interpret the hard-to-read signals across the health journey and unify these digital determinants of health with real-world data to produce the most dimensional view of the customer. Our award-winning advertising platforms use machine learning and programmatic automation to seamlessly activate this data, making marketing, predictive analytics, and decision support easy and instantaneous.
Sr. 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.
In addition to the above, you will work with the greater Data Science/Engineering teams on:
- 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.
- India, Netherlands, UK: we can hire as FTE
- Other countries: we can hire as long-term contractor
- Advanced knowledge of Python using standard DS packages (numpy/pandas/scikit, etc.); Being able to optimize and speed-up code.
- 3+ years of RTB Auction or similar online technologies.
- Algorithms and Data Structures (e.g., sorting, search tree, binary heap, trie; time & mem complexities of algorithms)
- 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.)
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