SherlockTalent
Machine Learning Engineer 100% Remote
SherlockTalent, Los Angeles, California, United States, 90079
Location: 100% Remote, work from anywhere in the world (Company based in South Florida)Role: Machine Learning EngineerCompensation: Depends on Experience, US $170K+ DOEJob Type: Full Time W2 or Fulltime Contract, C2CWe are looking for an entrepreneurial minded Machine Learning Engineer to help us disrupt the e-commerce industry. We believe it is time for technology and data to help Merchants deliver the best pricing to their customers, no matter what. If you are looking for an opportunity to join a start-up led by a proven founder in an industry that is growing exponentially, and an opportunity to work alongside a Chief Data Scientist and another ML Engineer, then this is the opportunity to work on something not only exciting and fun, but also creative in ways that most people will never experience in their lifetime.What You Will Do
You will work side by side with e-commerce experts and lead in the design and development of machine learning models to deliver optimal pricing for merchants. You will work on determining and capturing features best suited to achieve accurate predictions including but not limited to indicators of revenue, price and conversation rates.In this role you will develop and validate machine learning models, research relevant statistical methods, perform data analysis, and provide recommendations for operationalizing machine learning models into production.Who You Are
You are passionate about statistics and Machine Learning modeling. You are pragmatic and effective, looking for all possible ways of improving your models. You attack complex business questions with data and curiosity, diving below the surface to identify the root cause.You have a deep understanding of the different parts of the data pipeline, having been exposed to data analysis, data engineering, analytics engineering and data science. However, Machine Learning Engineering is what you want to focus on. You thrive working in a startup “get it done” environment and continuously delivering models into production to test makes you happy. You always deliver quality work but you embrace failure as a way to improve!A successful candidate must:
Be extremely hands-on and proficient working with Python (pandas, numpy, sklearn, etc).Experienced with plotting in Python (matplotlib, etc). Dashboarding experience is a plus.Be self-motivated and comfortable owning the entire lifecycle from beginning to end.Be able to understand the business needs first and then apply technology to solve those problems.Have experience aggregating data, exploring data, building & validating predictive models, and deploying completed models with concept-drift monitoring and retraining to deliver business impact to the organization.Have strong analytical skills.What You Need
Minimum 3-5 years working professionally in the machine learning and data analytics field.A higher education degree in computer science, mathematics, physics or data science is a major plus.Proven experience taking machine learning algorithms from R&D to product, especially using AWS cloud services.Proficient with SQL and data wrangling.Real world experience as an ML engineer or data scientist role building and deploying ML models or developing deep learning models, with experience in at least 3 of the 5 key machine learning algorithms: Ridge Regression and Lasso, random forests, linear optimization models, sensitivity analysis, and boosting model.To apply, please send us a brief paragraph about why you are interested and what you would do in your week. GitHub examples of your proudest projects or open-source contributions is a plus so please send along.
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You will work side by side with e-commerce experts and lead in the design and development of machine learning models to deliver optimal pricing for merchants. You will work on determining and capturing features best suited to achieve accurate predictions including but not limited to indicators of revenue, price and conversation rates.In this role you will develop and validate machine learning models, research relevant statistical methods, perform data analysis, and provide recommendations for operationalizing machine learning models into production.Who You Are
You are passionate about statistics and Machine Learning modeling. You are pragmatic and effective, looking for all possible ways of improving your models. You attack complex business questions with data and curiosity, diving below the surface to identify the root cause.You have a deep understanding of the different parts of the data pipeline, having been exposed to data analysis, data engineering, analytics engineering and data science. However, Machine Learning Engineering is what you want to focus on. You thrive working in a startup “get it done” environment and continuously delivering models into production to test makes you happy. You always deliver quality work but you embrace failure as a way to improve!A successful candidate must:
Be extremely hands-on and proficient working with Python (pandas, numpy, sklearn, etc).Experienced with plotting in Python (matplotlib, etc). Dashboarding experience is a plus.Be self-motivated and comfortable owning the entire lifecycle from beginning to end.Be able to understand the business needs first and then apply technology to solve those problems.Have experience aggregating data, exploring data, building & validating predictive models, and deploying completed models with concept-drift monitoring and retraining to deliver business impact to the organization.Have strong analytical skills.What You Need
Minimum 3-5 years working professionally in the machine learning and data analytics field.A higher education degree in computer science, mathematics, physics or data science is a major plus.Proven experience taking machine learning algorithms from R&D to product, especially using AWS cloud services.Proficient with SQL and data wrangling.Real world experience as an ML engineer or data scientist role building and deploying ML models or developing deep learning models, with experience in at least 3 of the 5 key machine learning algorithms: Ridge Regression and Lasso, random forests, linear optimization models, sensitivity analysis, and boosting model.To apply, please send us a brief paragraph about why you are interested and what you would do in your week. GitHub examples of your proudest projects or open-source contributions is a plus so please send along.
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