Position: Research Scientist III
Location: Onsite (Thornton, CO)
Duration: 3 Months with high possibility of conversion/extension
Job Description:
- Company strives to be Earth's most customer-centric company where people can find and discover virtually anything they want to buy online.
- By giving customers more of what they want - low prices, vast selection, and convenience - Company continues to grow and evolve as a world-class e-commerce platform.
- The AOP team is an integral part of this and strives to provide Analytical Capabilities to fulfil all customer processes in the IN-ECCF regions.
- We're seeking a Data Scientist with expertise in a breadth of ML techniques.
- Your responsibilities will include developing, prototyping and productionizing innovative models using a range of techniques (Supervised/Unsupervised/Reinforcement).
- We are also looking for innovators capable of using generative AI to design, evangelize, and implement state-of-the-art solutions for never-before-solved problems.
Key job responsibilities
- Demonstrate thorough technical knowledge on feature engineering of massive datasets, effective exploratory data analysis, and model building using industry standard AI/ML models and working with Large Language Models
- Proficiency in both Supervised (Linear/Logistic Regression) and Unsupervised algorithms (k means clustering)
- Understand the business reality behind large sets of data and develop meaningful solutions comprising of analytics as well as marketing management.
- Work closely with internal stakeholders like the business teams, engineering teams and partner teams and align them with respect to your focus area
- Innovate by adapting new modeling techniques and procedures
- Passionate about working with huge data sets (training/fine tuning) and be someone who loves to bring datasets together to answer business questions.
- You should have deep expertise in creation and management of datasets
- Exposure at implementing and operating stable, scalable data flow solutions from production systems into end-user facing applications/reports.
- These solutions will be fault tolerant, self-healing and adaptive.
- Work with distributed machine learning and statistical algorithms to harness enormous volumes of data at scale to serve our customers
Business Group & Key Projects:
- Entire team is under Global Last Mile Customer Experience team.
- They began in north America looking for customer feedback and branched out to start looking abroad in Europe and Japan.
- Right now, one pillar of team is focused on customer delivery feedback and how to expand in more of a global market.
- For this specific project, the other pillar they are focused on is dealing with Ship With pickups.
- There are certain processes where they have to go and pick up packages from places like staples, which is causing a headache and they are needing more visibility around that.
- The whole process is getting placed under Manager.
- Manager is wanting to make sure everything is built out and there are certain mechanisms in place to be able to track specific dashboards that are set up, workflows for how that looks both in start content and how it would look on certain mobile applications.
Basic Qualifications:
- 6+ years of data scientist or similar role involving data extraction, analysis, statistical modeling and communication experience
- Bachelor's degree in computer science, engineering, mathematics or equivalent
- Experience with statistical models e.g. multinomial logistic regression
- Proven knowledge of deep learning and experience hosting and deploying ML solutions (e.g., for training, tuning, and inferences)
Preferred Qualifications:
- Knowledge of AWS tech stack (e.g., AWS Redshift, S3, EC2, Glue)
- Master's degree in a quantitative field such as statistics, mathematics, data science, business analytics, economics, finance, engineering, or computer science
- Working knowledge of generative AI and hands on experience in deploying and hosting Large Foundational Models
Must Haves:
- Experience with statistical models e.g. multinomial logistic regression
- Proven knowledge of deep learning and experience hosting and deploying ML solutions (e.g., for training, tuning, and inferences)
- Bachelor's degree in computer science, engineering, mathematics or equivalent