Senior Machine Learning Engineer

About the job
Our Machine Learning Engineer role includes the following responsibilities:

Designing, developing, and researching cutting edge Machine Learning systems, models, and schemes in many different areas of Adtech
Developing real-time algorithms for exchange optimization
Discovering insights/patterns in exchange data, and developing methods to leverage/extract/process/analyze complex, high volume, high-dimensional datasets
Designing experiments, overseeing A/B testing, evaluating the quality of derived assets, and developing dashboards to continuously monitor model performance
Studying, transforming, and converting data science prototypes,
Searching and selecting appropriate data sets
Performing statistical analysis and using results to improve models
Training and retraining ML systems and models as needed
Identifying differences in data distribution that could affect model performance in real-world situations
Visualizing data for deeper insights
Analyzing the use cases of ML algorithms and ranking them by their success probability
Understanding when your findings can be applied to business decisions
Reducing business problems into optimization problems
Supporting, maintaining, and enriching existing ML frameworks and libraries
Verifying data quality and/or ensuring it via data cleaning

Collaborative Work

Work closely with product, engineering, and sales teams to drive the use of Data Science across Verve
Collaborate with our audience-focused Data Science team
Design end-to-end Data Science Products, working closely with the data and backend engineering teams
Collaborate with teams across the globe in different time zones
Extend successful solutions and optimization to sister companies in Verve
Job requirements

What You Will Bring

5+ years experience working as a Data Scientist or ML Engineer in industry (preferably in advertising) is a must, where you have gained hands-on experience delivering data science products
A postgraduate degree in a relevant quantitative field (e.g. applied mathematics, statistics, engineering, computer science, physics, operations research, economics, behavioral sciences). PhD is a strong plus
Experience in taking data science projects from concept to production in an industry setting
Fluent in statistical analysis, data mining, and machine learning
Strong application and ML development experience in Python, functional as well as object oriented programming
Proficient in SQL, PySpark, TensorFlow, Sklearn
Experience with developing or supporting high-performance APIs
Enthusiasm for solving interesting problems
Willingness to learn and work in a team-oriented and constantly changing environment