Principal Data Scientist

Role Description

The Principal Data Scientist will be responsible for leading advanced data science projects and implementing statistical models and machine learning algorithms for large-scale data analysis.

• Design and implement robust statistical models and machine learning algorithms for large-scale data analysis and predictive analytics.
• Lead end-to-end development of data science projects, including hypothesis testing, regression analysis, classification, and forecasting.
• Collaborate with cross-functional teams to define business requirements, translate them into analytical solutions, and drive measurable impact.
• Optimize and automate data pipelines using Python, PySpark, and R, ensuring efficient data processing and feature engineering.
• Develop, validate, and maintain probabilistic graph models and advanced statistical computing frameworks.
• Utilize industry-leading ML frameworks such as TensorFlow, PyTorch, and Sci-Kit Learn to build, train, and deploy models.
• Establish rigorous model evaluation and monitoring processes using tools like Great Expectations and Evidently AI.
• Mentor and guide junior data scientists, fostering technical excellence and continuous learning within the team.

Qualifications

• Master’s or PhD degree in Data Science, Statistics, Computer Science, Mathematics, or a related field.
• Relevant certifications in machine learning, statistical analysis, or advanced data science.

Requirements

• 15 - 18 years of experience in advanced data science roles, with extensive leadership in designing and deploying statistical and machine learning solutions.
• Expertise in hypothesis testing, including T-Test and Z-Test methodologies.
• Advanced proficiency in regression techniques (linear and logistic).
• Strong programming skills in Python, PySpark, and R/R Studio.
• Hands-on experience with SAS and SPSS for statistical analysis and computing.
• In-depth knowledge of probabilistic graph models.
• Experience with forecasting methods such as Exponential Smoothing, ARIMA, and ARIMAX.
• Practical use of classification algorithms including Decision Trees and Support Vector Machines (SVM).
• Proficiency with ML frameworks: TensorFlow, PyTorch, Sci-Kit Learn, CNTK, Keras, MXNet.
• Familiarity with distance metrics (Hamming, Euclidean, Manhattan).
• Working knowledge of Kubeflow and BentoML for model deployment and orchestration.

Benefits

• $180,000 - $190,000 a year.