| Machine Learning Engineer or Data Engineer,No H1B at Remote, Remote, USA |
| Email: [email protected] |
| From: Shikha, KPG99 [email protected] Reply to: [email protected] Hi, Hope you are doing well. Please find the job description below and let me know your interest. Position : Machine Learning Engineer or Data Engineer, No H1B Location: Hybrid in Plano, TX or Pleasanton, CA Duration: 6+ Month MOI: Phone and Video Interview Process 2 interviews. 1st with engineering manager or Aravind & 2nd will be technical from Tech lead or architect. 30 min for 1st round 45 min for 2nd round Reason for position Looking to expend the team by 20% Team Overview The team is spread between 4 PODS that are mix skill set between ML and Data Engineer Project Description 2 main projects: Personalization focused on customer how do we solve customer related problems. Products/content, putting the right product/content in front of the customer. The team call Customer Team Business growth anything that is related to Supply Chain, inventory, price, merchandising In both of these fields/projects, Aravind works closely with Data Science team to put the Data Science model into production, which means Aravinds team, build the pipelines end to end which includes data preparation and data cleaning, training, model evaluation, model metrics. Duties/Day to Day Overview This Engineer will be responsible for sending the model to production which means creating the pipeline which can run the task, training and put it into production. Someone else would have to consume it and it would have to be expose to some serving API layer. Top Requirements (Must haves) Having ML Engineering background and understanding the ML Lifecycle experience is critical Candidate needs to understand at what stage what is happening and what needs to be done Need someone who is strong technically, using spark as a way of using distributed framework and having deep knowledge in that. If using data from snowflake or maybe using data from data lake, the candidate will need to understand how the data is organized how you structure the data in order to have an optimal read. How do you write queries to make sure the performance of the data that you are reading on the read queries is optimal. The 2nd aspect we look in the candidate, once these models are done it's some other consuming application need to consume it. Example, customer if you now producing recommendations for the customer it needs to be shown to different end, on an app or mobile application so we are looking at integrations working closely with those teams around building the integration points so that's becomes like a software engineering skill set. This is NOT a Data Science role , they will not be looking at what model and what algorithm that they need to solve the business part. Not looking for data science who understand the model itself Additional Qualifications (Nice to Haves) If a candidate has a data science experience but willing to become a ML Engineer,: The sample candidate of a resume that we reviewed had the following skill set: Has data analysis experience Data imputation using Scikit-learning package in Python with Spark with 2.0 Used predictive models using machine learning algorithms Used Random Forest, K-mean Clustering Used XGboost to predict customer life time value AWS with primary with Spark Although the title shows as Data Scientist but it does not look like he's data scientist. Normally data scientists are really exploring doing an exploratory study and figuring out what model to use and they are always stick to one specific model. Unlike this profile he uses many models in one project Additional Notes Will be nice if candidate comes from: Retail, supply chain, merchandising, pricing optimization and personalization. Platform that is currently being used by this team Spark Python Data lake snowflake Azure (cloud) Keywords: machine learning information technology California Texas |
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| Fri Jul 28 00:52:00 UTC 2023 |