Principal Data Scientist – Generative AI

Principal Data Scientist – Generative AI

Requirements

• A Master’s degree (or higher) in Computer Science, Business Analytics, Data Science, Management Information Systems, Engineering, or another quantitative discipline OR a Bachelor’s degree with extensive industry experience
• 5-7 years of relevant work experience in data science, machine learning, or AI, OR extensive applied research experience through a doctoral program
• Strong understanding of statistical and machine learning techniques, including classification, regression, dimensionality reduction, regularization, clustering, multivariate analysis, feature engineering, and hyperparameter tuning
• Proficiency in Python for large-scale data analysis, with experience in open-source data science toolkits such as Pandas, SciPy, and Scikit-Learn
• Hands-on experience with data visualization tools like Matplotlib, Seaborn, Plotly, or BI platforms like Power BI or Tableau
• Proficiency in distributed computing frameworks like Spark or Ray for scalable data processing
• Experience with relational data modeling and SQL, including data retrieval, transformation, and optimization for analytical applications
• Experience with model monitoring and CI/CD pipelines, including model versioning, automated retraining, and performance tracking in production environments
• Expertise with cloud computing and MLOps, including AWS (S3, Lambda, SageMaker, Athena) or equivalent cloud platforms (GCP or Azure) with knowledge of model deployment best practices
• Strong analytical and problem-solving skills, with the ability to translate theoretical concepts into practical solutions by designing, prototyping and deploying scalable machine learning solutions
• Excellent communication and storytelling skills, including the ability to distill complex insights into actionable business recommendations through written, oral, and visual presentations

Responsibilities

• Drive strategic data science initiatives, influencing how machine learning and analytics shape key business decisions
• Analyze large-scale datasets, extract deep insights, and develop advanced data visualization strategies that drive measurable impact and provide measurable recommendations
• Architect scalable data pipelines to organize, process, and clean large batches of text, image, and geometric data for downstream analytics and machine learning
• Lead experimentation and advanced statistical modeling, designing robust methodologies to validate hypotheses and optimize business outcomes
• Develop analytical solutions and interactive dashboards that integrate data from various sources, including relational databases and AWS
• Develop, deploy, and operationalize machine learning models, ensuring scalability, reliability, and real-world effectiveness
• Mentor and guide other data scientists, fostering a culture of knowledge sharing, collaboration, and continuous learning
• Collaborate cross-functionally with engineering, product, and business teams to define and execute data-driven strategies
• Stay ahead of emerging trends in AI and data science, proactively identifying opportunities to integrate cutting-edge techniques into business applications
• Define and promote best practices and frameworks for data science workflows, model deployment, and responsible AI usage

Preferred Qualifications

• Background in Architecture, Engineering, or Construction
• Deep learning experience, with hands-on proficiency in frameworks like TensorFlow or PyTorch for designing, training, and deploying neural networks
• Experience leading cross-functional teams and mentoring data scientists, fostering a culture of innovation and knowledge sharing
• Familiarity with responsible AI principles, including bias mitigation, explainability, and ethical AI practices
• You are passionate about solving problems for AEC customers (Architecture, Engineering, and Construction) by applying machine learning techniques
• You are a strategic thinker, capable of shaping and executing long-term data science initiatives that align with business objectives
• You are comfortable working in newly forming ambiguous areas where learning, experimentation and adaptability are key skills
• You actively contribute to a learning-driven culture, sharing knowledge, mentoring peers, and fostering an environment of continuous growth
• You are bold and iterative, unafraid to share ideas, experiment, and fail fast