Lead ML Engineer

Role Responsibilities:
Data prep for machine learning models (discovering data sources, accessing data, transforming data and cleaning data)
Work with data scientists on building ML pipelines and productionsize them
Partner with data scientists to understand, implement, refine and design machine learning algorithms and ML pipelines. Iterating and failing fast is critical.
Run regular A/B tests, gather results, perform statistical analysis, draw conclusions on the impact of your models.
Work cross functionally with product managers, data scientists and product engineers, and communicate results to peers and leaders.
Explore latest innovations and developments around Machine Learning Platforms and open source frameworks, evaluate them and help in adoption
Qualifications: 
MS or PhD degree in Computer Science, ML or related field. 
Minimum 5 years of relevant industry experience.
Programming languages, frameworks and tools :
Strong skills: Python, Spark, Java, Kafka/Kafka Stream, Kubernetes
Good to have: Airflow(or equivalent orchestration frameworks), Deep Learning, Scikit, NLTK, Numpy, Pandas, TensorFlow, Keras, R
Basic knowledge of machine learning techniques (i.e. classification, regression, and clustering).
Knowledge of common data query and data processing tools (i.e. SQL)
Good computer science fundamentals - data structures, algorithms, performance complexity, and implications of computer architecture on software performance (e.g., I/O and memory tuning).
Experience of building, deploying and operating ML pipelines at massive scale using batch and streaming big data technologies in Kubernetes.
Experience of working on anomaly detection, security products and fraud detection will be a plus
Experience on the tooling of python applications will be a plus