Explore current openings to see how your skills and interests align with Target careers.
Lead Data Scientist - Pittsburghapply now Job ID BUS00014A Date posted 06/15/2017 Location Pittsburgh, Pennsylvania – United States
The Target Pittsburgh Strategy and Innovation office is expanding our team of hard-working and talented Data Scientists and Engineers. With billions of store and online guests daily, Target is now leading the effort in tearing down the walls between the traditional brick-and-mortar and Web retail business. Just as established e-commerce giants are beginning to ramp up their physical store presence, Target is already leveraging its massive supply chain of over 1,800 stores 38 distribution centers worldwide to deliver the best experience to its guests.
But with huge amounts of both foot and web traffic comes a host of challenging problems: How do you link Web and store behavior to create a cohesive view of a guest? How do you translate this heterogeneous data into actionable decisions in real-time? How can in-store experience be enhanced by mobile and vice versa?
As a data scientist in the Pittsburgh Strategy and Innovation Office, you will work as part of a small team to answer these types of questions and more, all with the potential to have a very broad impact at Target. Our office combines the freedom and agility of a startup with the security and vast resources of a large established company.
- BS degree; engineering/quantitative field.
- 2+ year’s academic or professional experience in building ML models.
- Expert level in at least one ML framework (e.g., MATLAB/R/scikit-learn/MLLib).
- Experience with ML on map/reduce big data systems (e.g., Hadoop/Spark).
- Version control, particularly Git.
- Experience with SQL languages.
- Broad understanding of object-oriented and functional programming paradigms.
- Comfortable with at least one scripting language (e.g., bash/python).
- Comfortable in *nix environment (e.g., ssh and standard commands).
- Excellent verbal and written communication skills.
- Depth in at least one theoretical area of ML (DNNs, spectral learning, etc.)
- Depth in one application of ML (NLP, QA, CV, etc.)
- Experience with Scala and/or Spark.
- Advanced in at least one functional programming language.
- Experience in a JVM language.
- Experience with NoSQL data stores.
- Contributions to large open-source projects.
- Master's in data science or related field.
- PhD in any quantitative field.