At Scale AI, we are accelerating the development of artificial intelligence by providing the essential, data-centric infrastructure for building and deploying advanced AI systems. We are looking for a Forward Deployed Data Scientist/Engineer to architect and build the sophisticated data solutions that solve our customers' most complex challenges. This role is for a first-principles thinker who thrives on ambiguity, enjoys solving novel customer problems, and has a passion for applying rigorous data science to deliver measurable business impact.
You'll work on a mix of cutting-edge, customer-facing AI implementations. Our team powers projects like TIME’s Person of the Year AI experience, where our technology helped shape one of the most iconic features in media. Leveraging Scale's powerful data infrastructure, you will build bespoke evaluation frameworks, deploy custom statistical models, and architect data pipelines. Your work will play a crucial role in shaping how AI delivers measurable value in real-world applications for industry-leading companies.
You'll be exposed to the cutting edge of the Generative AI industry while directly interfacing with leading enterprise organizations, including top industry leaders in telecommunications, e-commerce, finance, education, publication, insurance, and health.
Responsibilities:
- Drive Impact: Directly contribute to the advancement of AI by delivering critical data science infrastructure and insights for leading AI innovators and enterprise customers.
- Customer Collaboration: Interact daily with our technical customers, deeply understanding their unique challenges and translating ambiguous business problems into concrete data-driven solutions and robust statistical models.
- End-to-End Data Solution Development: Design, build, and deploy solutions across the entire data lifecycle—from data ingestion and pipeline construction to statistical modeling, experimentation, model evaluation, and insight generation.
- Rapid Experimentation: Design and deliver high-quality experiments (e.g., A/B tests, marketplace modeling), iterating quickly to validate hypotheses, meet customer needs, and drive innovation.
- Strategic Influence: Act as the voice of the customer to our internal Product, Engineering, and Data Science teams, channeling real-world insights to influence our product roadmap and technical strategy.
- Diverse Projects: Engage in a dynamic mix of designing and deploying cutting-edge data solutions, from building LLM evaluation frameworks to adapting statistical models for novel economic and business problems.
- Leadership Growth: This role offers a unique opportunity to lead critical customer-facing projects, shape our data culture, and accelerate your career growth. You'll be positioned to become a future leader in a company defining the next era of technology.
Requirements:
- 5+ years of relevant industry experience in a highly analytical role (e.g., Data Science, ML Engineering, Quantitative Analysis).
- Proven track record of shipping high-quality data products, models, or features at scale.
- Strong problem-solving skills and the ability to turn abstract business and product ideas into concrete data science and engineering solutions.
- Expert-level coding abilities in Python for data science (e.g., Pandas, NumPy, Scikit-learn) and mastery of complex SQL across large datasets.
- Ability to effectively communicate complex technical concepts to both technical and non-technical audiences.
- Desire to thrive in a fast-paced, dynamic environment and adapt quickly to the ever-changing world of Generative AI.
- Excited to join a dynamic, hybrid team in either San Francisco or New York City.
Preferred Qualifications:
- Experience in a client-facing or consultative role (e.g., Forward Deployed Engineer, Solutions Architect, Data Science Consultant).
- Deep expertise in designing metrics, diagnosing data inconsistencies, and building evaluation frameworks for ML/LLM systems.
- Experience with large-scale data processing frameworks and distributed systems (e.g., Spark, Ray).
- Familiarity with marketplace experimentation, causal inference, and advanced statistical modeling.
- Experience with cloud-based infrastructure and data warehousing (e.g., AWS, GCP, Snowflake, BigQuery).
