Remote Data Scientist, Auction Mechanics/AdTech (Remote) with verification

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.

Data Scientist, Auction Mechanics

As a member of our Data Science Engineering team, the Data Scientist, Auction Mechanics 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.

Location: Anywhere in the world (End days at around 1-2pm EST)

Requirements:

• 3 years minimum of experience in data science

Key Skills: Python, Algorithms, Optimisation, NLP, Data Mining, Statistical Analysis, Neural Networks, Generalised Linear Regression, Multiclass Classification, Java, R

• 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.

In addition to the above, you’ll need to have strong knowledge in the following areas:

• 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.)

WebMD and its affiliates is an Equal Opportunity/Affirmative Action employer and does not discriminate on the basis of race, ancestry, color, religion, sex, gender, age, marital status, sexual orientation, gender identity, national origin, medical condition, disability, veterans status, or any other basis protected by law.