Manager, Strategic Data Science

To get the best candidate experience, please consider applying for a maximum of 3 roles within 12 months to ensure you are not duplicating efforts.

Job Category

Job Details

About Salesforce

We're Salesforce, the Customer Company, inspiring the future of business with AI+ Data +CRM. Leading with our core values, we help companies across every industry blaze new trails and connect with customers in a whole new way. And, we empower you to be a Trailblazer, too - driving your performance and career growth, charting new paths, and improving the state of the world. If you believe in business as the greatest platform for change and in companies doing well and doing good - you've come to the right place.

Role Description: As a Strategic Data Scientist, you will own the end-to-end design, development, and production deployment of advanced AI and data-driven solutions. You'll build scalable machine-learning models with large, heterogeneous datasets to solve complex business challenges and provide proactive, data-driven guidance to our Customer Success organization.

Key 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

• Present clear, actionable insights and technical roadmaps to technical and non-technical stakeholders at all levels

Collaborative Partners:

• Customer Success Leadership: define priority use cases and success metrics for AI-driven initiatives

• Product & Engineering: embed data-science solutions into product features and roadmaps

• Data Platform & MLOps: utilize internal infrastructure for data access, orchestration, and scalable deployments

• Business Operations & Finance: validate model assumptions, quantify ROI, and support strategic planning

Role Requirements:

• Education: Bachelor's or Master's in quantitative field such asData Science, Computer Science, Statistics, Mathematics, Engineering, or a related discipline

• Experience: 2-5 years of hands-on experience building and deploying machine-learning solutions-especially recommender systems-in a SaaS or customer-facing environment

• Technical Proficiency: 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)

• AI & Next-Gen Models: Demonstrated experience with embedding techniques, transformer-based models, and graph ML for large-scale recommendations

• Business Acumen: Strong analytical mindset; able to translate model outputs into clear business recommendations and track impact through defined KPIs

• Communication & Influence: Excellent at distilling complex technical concepts for non-technical audiences and driving alignment across teams

• Self-Starter: Thrives in ambiguous environments; owns projects end-to-end and iterates based on feedback

Preferred Qualifications:

• Enterprise-Scale Recommenders: Previous hands-on experience building and scaling recommender systems at major technology platforms (e.g., Meta/Facebook/Instagram, Netflix)

• Top-Tier Consulting Background: Prior experience at a leading strategy firm (e.g., McKinsey & Company, Bain & Company, BCG) with demonstrated ability to translate complex analysis into clear recommendations

• LLM Proficiency: Hands-on experience leveraging large language models (e.g., GPT-4) for data augmentation, prompt engineering, or analytics automation

• Advanced AI Use Cases: Proven track record of applying cutting-edge techniques-transformer fine-tuning, embedding retrieval, graph neural networks- to build production recommender or decision-support systems

Accommodations

If you require assistance due to a disability applying for open positions please submit a request via this Accommodations Request Form.

Posting Statement

Salesforce is an equal opportunity employer and maintains a policy of non-discrimination with all employees and applicants for employment. What does that mean exactly? It means that at Salesforce, we believe in equality for all. And we believe we can lead the path to equality in part by creating a workplace that's inclusive, and free from discrimination. Know your rights: workplace discrimination is illegal. Any employee or potential employee will be assessed on the basis of merit, competence and qualifications - without regard to race, religion, color, national origin, sex, sexual orientation, gender expression or identity, transgender status, age, disability, veteran or marital status, political viewpoint, or other classifications protected by law. This policy applies to current and prospective employees, no matter where they are in their Salesforce employment journey. It also applies to recruiting, hiring, job assignment, compensation, promotion, benefits, training, assessment of job performance, discipline, termination, and everything in between. Recruiting, hiring, and promotion decisions at Salesforce are fair and based on merit. The same goes for compensation, benefits, promotions, transfers, reduction in workforce, recall, training, and education.