ML Data Scientist
Location: Plano, TX (Hybrid)
Role Overview
In this role, you will help drive personalized experiences for customers as an ML Engineer monitoring pipelines, batch, and on-demand job types to improve Pricing Recommendations.
Are you passionate about harnessing the power of data to drive impactful business decisions? We are seeking an ML Engineer to play a pivotal role in shaping the future of our consumer journey through cutting-edge Machine Learning (ML) solutions for pricing and recommendations. Join us and make a tangible difference in the lives of our customers by developing innovative solutions that deliver personalized experiences and optimize value.
This is a remote and contract-only position.
About The Role
- Ability to attend meetings and discussions during overlapping XXXX Standard Time (XST) hours (must-have).
- ML acumen to conceptualize, design, and implement state-of-the-art ML models for dynamic pricing strategies and personalized product recommendations.
- Strong grasp of understanding the difference between data pipelines and ML pipelines.
- Develop, implement, and deploy machine learning models that leverage our unique combination of user behavior and subscription data to improve consumer value from our products.
- Engineer and maintain large-scale consumer behavioral feature stores while ensuring scalability and performance.
- Develop and maintain data pipelines and infrastructure to support efficient and scalable ML model development and deployment.
- Collaborate with cross-functional teams (Marketing, Product, Sales) to ensure your solutions align with strategic objectives and deliver real-world impact.
- Create algorithms for optimizing consumer journeys and increasing conversion and monetization.
- Design, analyze, and troubleshoot controlled experiments (Causal, A/B tests, Multivariate tests) to validate your solutions and measure their effectiveness.
- Agile development mindset, appreciating the benefit of constant iteration and improvement.
- Focus on business practicality and the 80/20 rule; very high bar for output quality, but recognize the business benefit of having something now vs. perfection sometime in the future.
About You:
- Bachelor's degree in Computer Science or related fields; Master's or PhD in Machine Learning, Statistics, Data Science, or related quantitative fields preferred.
- 5 years of experience in one or more of the following areas: machine learning engineering, including deep learning, recommendation systems, pattern recognition, data mining, or artificial intelligence.
- Proficient in Python, SQL; intermediate data engineering skill set with tools, libraries, or frameworks such as MapReduce, Hadoop, Spark, Hive, and Big Data technologies (scikit-learn, Keras, TensorFlow, PyTorch, PySpark, etc.).
- Experience in Databricks is preferred.
- Experience with various ML techniques and frameworks (e.g., data discretization, normalization, sampling, linear regression, decision trees, deep neural networks, etc.).
- Experience in building industry-standard recommender systems and pricing models.
- Experience in MLOps, ML Engineering, and Solution Design.
- It's Great But Not Required If You Also Have:
- Experience working in a consumer or B2C space for a SaaS product/software provider.
- Experience in developing recommendation systems and deep learning-based models.
- Excel in solving ambiguous and complex problems, being able to navigate through uncertain situations, breaking down complex challenges into manageable components, and developing innovative solutions.
Skills:
Machine Learning, Python