Description

Function: Engineering, R&D 1 Data Science / Machine Learning / Operations Research

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.

Location: anywhere in the world (End days at around 2pm ET)

• India, Netherlands, UK: we can hire as FTE
• Other countries: we can hire as long-term contractor

Requirements:

5 years minimum of experience in machine learning/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, youll 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.)

Selection Process:

1) Initial Screening Call (30 mins)

2) Technical Pre-Screening Call with Principal Data Scientist (60 mins)

4) Team Interview (around 4-5 hours total)

5) WebMD/IB Sr. Tech Leader (30 mins)