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Job Description & How to Apply Below
As a Strategic Data Scientist at Keystone you will design and deploy analytical models, machine learning systems, and investigative workflows that support expert testimony, litigation strategy, and regulatory advisory in areas such as AI, cybersecurity, and platform governance. Working alongside economists, fellow technologists, and strategists you’ll transform complex, often unstructured datasets e.g., JSON, CSV, logs, scraped data into defensible insights that drive decisions in high-impact cases involving antitrust, digital risk, and emerging technology regulation.
Key Responsibilities- Lead the design and implementation of analytical solutions from data architecture to model deployment.
- Apply statistical, econometric, and machine learning techniques to generate insights in areas such as AI risk, digital forensics, and competition analysis.
- Build, test, and operationalize machine learning models, large language models (LLMs), and analytical pipelines that scale across projects.
- Collaborate with consultants, economists, and legal experts to translate technical findings into strategic recommendations.
- Serve as a technical advisor on engagements involving advanced data, AI, and digital transformation topics.
- Mentor and guide junior engineers and data scientists in best practices for modeling, code development, and experimental design.
- Contribute to the development of internal tools, libraries, and frameworks that enhance analytical consistency and scalability.
- Engage in knowledge-building initiatives that strengthen Keystone’s global AI and data science community.
- Advanced graduate degree (PhD or Master’s) in Computer Science, Engineering, Statistics, Applied Mathematics, or a related technical field.
- At least 6 years of professional experience in data science, machine learning, or applied analytics roles.
- At least 2 years of experience working in the GCC.
- Deep understanding of AI/ML, NLP, and data science methodologies with experience applying them in real-world or regulated domains.
- Strong proficiency in Python, R, SQL, and familiarity with modern data science ecosystems (e.g., PyTorch, Tensor Flow, scikit-learn, Hugging Face, OpenAI API).
- Hands‑on experience with cloud platforms (AWS, GCP, or Azure) and scalable data workflows (Airflow, dbt, Spark, Big Query, Snowflake).
- Ability to manage multiple analytical work streams and communicate complex findings clearly to technical and non-technical audiences.
- Proven leadership in guiding diverse technical teams and driving innovation in data‑driven problem solving.
- A balance of technical excellence, intellectual curiosity, and entrepreneurial spirit suited to a fast‑moving global consulting environment.
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