Salesforce is looking to solve complex business challenges and provide proactive, data-driven guidance to their Customer Success organization using advanced AI and data-driven solutions.
Requirements
- Proficient in Python (or R) and ML frameworks (scikit-learn, TensorFlow, PyTorch); expertise with data tools (SQL, Spark, Airflow) and cloud platforms (AWS, GCP, Azure)
- Demonstrated experience with embedding techniques, transformer-based models, and graph ML for large-scale recommendations
- Experience with building and deploying machine-learning solutions—especially recommender systems—in a SaaS or customer-facing environment
- Hands-on experience leveraging large language models (e.g., GPT-4) for data augmentation, prompt engineering, or analytics automation
- Proven track record of applying cutting-edge techniques—transformer fine-tuning, embedding retrieval, graph neural networks— to build production recommender or decision-support systems
- Previous hands-on experience building and scaling recommender systems at major technology platforms
- Prior experience at a leading strategy firm with demonstrated ability to translate complex analysis into clear recommendations
Responsibilities
- Collaborate with customer success, product, engineering, and sales teams to define KPIs and analytical approaches that answer key business questions
- Design, build, and deploy machine learning and AI models (classification, regression, NLP, recommendation engines, etc.) to identify at-risk customers, predict attrition, and assess impact of product offerings
- Develop customized recommendation engines that suggest next-best actions for customers (collaborative filtering, content-based, hybrid, graph-based techniques, etc.)
- Drive the end-to-end machine learning lifecycle, from data preprocessing and feature engineering to model training, testing, and automated retraining workflows
- Architect high-performance data pipeline for massive, multi-source datasets (streaming, batch, semi-structured), ensuring optimal storage, fast query performance, and high data integrity in hybrid cloud environments
- Monitor production model performance by tracking key metrics like accuracy, drift, and latency. Leverage A/B testing and establish feedback loops to drive continuous improvement and rapid iteration
- Support translation of strategic direction into analytical problems and actionable data science initiatives, ensuring data science alignment with organizational goals and long-term vision
Other
- Bachelor’s or Master’s in quantitative field such as Data Science, Computer Science, Statistics, Mathematics, Engineering, or a related discipline
- 2–5 years of hands-on experience building and deploying machine-learning solutions
- Strong analytical mindset; able to translate model outputs into clear business recommendations and track impact through defined KPIs
- Excellent at distilling complex technical concepts for non-technical audiences and driving alignment across teams
- Thrives in ambiguous environments; owns projects end-to-end and iterates based on feedback
