Machine Learning Engineer

A fast-growing supply chain SaaS firm that is revolutionizing how businesses manage suppliers, control production, and track product data is looking for a Machine Learning Engineer. The selected candidate will be in charge of building new features and supporting the existing platform. During their Series A round, the company raised funding worth more than $10 mn and has since gained patronage from some of the most recognized D2C brands in the consumer-packaged goods space. This will be an excellent opportunity for developers to expand their horizons and pursue rewarding career opportunities.

Job Responsibilities:

• Productionize Machine Learning models into product features
• Own project timelines, and prioritize tasks with the user and business impact in mind
• Collaborate with the team and work on assessing speed/precision tradeoffs
• Develop ML models to identify supply chain risks
• Learn new methods, approaches, and perspectives in data science
• Develop NLP models to facilitate communications between brands and suppliers
• Find and integrate data from external sources to form the foundation of a new data product
• Craft out workflows and tools to organize the development of the latest ML models
• Proactively communicate project statuses to stakeholders and teammates

Job Requirements:

• Bachelor’s/Master’s degree in Engineering, Computer Science (or equivalent experience)
• At least 3+ years of relevant experience as an ML engineer
• Experience developing ML models like Classification/Regression Models, NLP models, and Deep Learning models
• Prolific data wrangling skills and ability to handle complex datasets using SQL
• Familiar with Python and related data libraries like Pandas, Numpy, Scipy, and NLTK
• Experience working with cloud-based data warehouse technology such as BigQuery and Amazon Redshift
• Prior experience with large-scale data processing and distributed systems like Redshift and Spark/Dask
• Prior experience with data processing, feature development, and model optimization
• Core competency in statistics - forecasting, time series, hypothesis testing, classification, etc.
• Ability to understand and evaluate machine learning models