About The Role
The Principal Applied Scientist will be pivotal in advancing the Pricing team's objectives by leading key applied science projects that address complex business challenges from initial list price, discounting, and promotions. This role involves close collaboration with Product and Engineering partners to develop technical roadmaps and deliver Machine Learning solutions that drive impactful OKR's, such as improving model performance, enhancing pricing-related revenue generation and optimizing the efficiency of model deployment pipelines.
- States Not Eligible: AK, AR, DE, KS, MS, ND, SD, WY
What You Get To Do Every Day
- Serves as a subject-matter expert (SME) in one or two domains, such as econometrics, algorithmic pricing and bidding, marketing science, and causal inference models.
- Partner closely with cross-functional teams to ensure alignment of ML solutions with broader business goals.
- Lead the design, development and deployment of Machine Learning models that solve key/strategic business problems, focusing on scalability, reliability, and performance.
- Develop and maintain clean, efficient, and scalable code that meets industry standards. Ensure code is well-documented and easily accessible for future iterations and optimizations, fostering best practices in coding and model deployment.
- Conduct deep analyses on complex datasets to derive actionable insights, employing state-of-the-art methodologies such as deep learning frameworks (e.g., TensorFlow, PyTorch), counterfactual reasoning, and causal inference.
- Utilize cutting-edge ML methodologies and frameworks to develop robust, scalable models that solve high-impact pricing and discounting problems, enhancing our ability to predict and optimize product pricing with a high degree of accuracy.
- Influence technical direction and take ownership of key components within the pricing and discounting ecosystems, ensuring solutions are built to support current and future business needs.
- Collaborate with key stakeholders in the development of data-driven solutions and deployable products. Contribute to the development of technical roadmaps and product initiatives.
- Provide mentorship to junior and mid-level ML engineers, fostering team expertise in pricing-related ML domains.
- Contribute to the company's intellectual property and technical leadership through patents and publications at top-tier conferences and journals.
What You Bring To The Role
Minimum Requirements:
- 10+ years of industry experience in applied Machine Learning, including a proven track record in designing, deploying, and scaling production-level ML models.
- Master's or PhD in AI, Computer Science, Econometrics, Mathematics, Statistics, Electrical Engineering or related field.
- 8+ years experience in building, deploying, and managing machine learning models in production environments at scale, with a focus on pricing, discounting, algorithmic bidding, or similar complex domains.
- Extensive knowledge of ML best practices (A/B testing, experiment design, training/serving pipelines, feature engineering) and advanced ML algorithms/techniques (gradient boosting, deep neural networks, optimization, regularization).
- Experience in at least one of these domains: price optimization, discounting/promotions, algorithmic bidding.
- Extensive experience in scientific and ML libraries in Python (NumPy, Pandas, Scikit-Learn) and deep learning frameworks (Tensorflow, Keras, PyTorch).
- Strong data engineering skills and experience working with large scale datasets, including data preprocessing, feature extraction, and efficient data handling.
- Hands-on experience with big data tools (Apache Beam, Apache Kafka, Spark) for distributed processing of large datasets.
- Proficiency with cloud platforms (AWS, GCP, or Azure) for scalable model deployment and data storage solutions.
- Fluency in Python and SQL for data manipulation, querying, and analysis.
Preferred Requirements:
- PhD in Computer Science, Machine Learning, Econometrics, AI or related field.
- Strong background in applying Machine Learning techniques to solve real-world business problems in the retail or e-commerce sector.
- Hands-on experience with MLOps tools and pipelines, enabling smooth model lifecycle management.
- Impact-focused mindset, with a commitment to delivering high-quality, business-oriented ML solutions.
- Demonstrated leadership and mentoring skills, with experience leading and inspiring technical teams.
Compensation, Benefits, + Perks
- Employee Stock Purchase Plan
- 401K with Company Match
- Medical, Dental & Vision Insurance
- Paid Parental Leave
- 9 Paid Company Holidays
- Flexible Time Off (With Manager Approval)
- Find out more about our Benefits here.
The expected salary range for this role is $249,734.00-$277,482.00. To determine starting pay we carefully consider a variety of factors, including primary work location and an evaluation of a candidate's skills, experience, market demands, and