Machine Learning Generalist, Remote Position Quick Overview

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Role : ML Generalist Full Stack

Location : Remote

Duration : 6+ Months

Role Overview

Are you passionate about harnessing the power of data to drive impactful business decisions We are seeking a ML Generalist to play a pivotal role in shaping the future of our consumer journey through cuttingedge Machine Learning ML solutions for pricing and recommendations Join us and make a tangible difference in the lives of our customers by developing innovative solutions that deliver personalized experiences and optimize value

About The Role

Ability to attend meetings and discussions during overlapping XXXX Standard Time XST hours musthave
Full Stack ML acumen to conceptualize design and implement stateoftheart ML models for dynamic pricing strategies and personalized product recommendations
Develop implement and deploy machine learning models that leverage our unique combination of user behavior and subscription data to improve consumer value
Engineer and maintain largescale consumer behavioural feature stores while ensuring scalability and performance
Develop and maintain data pipelines and infrastructure to support efficient and scalable ML model development and deployment
Collaborate with crossfunctional teams Marketing Product Sales to ensure your solutions align with strategic objectives and deliver realworld impact
Create algorithms for optimizing consumer journeys and increasing conversion and monetization
Design analyze and troubleshoot controlled experiments Causal AB tests Multivariate tests to validate your solutions and measure their effectiveness
Agile development mindset appreciating the benefit of constant iteration and improvement
Focus on business practicality and the 8020 rule very high bar for output quality but recognize the business benefit of having something now vs perfection sometime in the future

About You

Masters Degree PhD in Machine Learning Statistics Data Science or related quantitative fields preferred
3 to 5 years of experience in one or more of the following areas machine learning including deep learning recommendation systems pattern recognition data mining or artificial intelligence
Proficiency in using casual inference uplift modeling splines support vector machines lookalike modeling model stacking ensembles embeddingbased modeling etc
Proficient in Python SQL intermediate data engineering skill set with tools libraries or frameworks such as PySpark Hadoop Hive and Big Data technologies scikittearn pandas numpy PyTorch
Experience with various ML techniques and frameworks eg data discretization normalization sampling linear regression decision trees deep neural networks etc
Experience in building industrystandard recommender systems and pricing models
Experience in MLOps ML Engineering and Solution Design