Principal Data Scientist

About the Role

Key Responsibilities

• Data Extraction and Preparation: Collect data from various structured and unstructured sources (datalakes, databases, data warehouses, on cloud, internal, external) and ensure its quality for analysis through cleaning and preprocessing.
• Designs, builds, and analyzes large (e.g., 100's of Terabytes or higher) and complex data sets while thinking strategically about data use and data design.
• Create relevant features and conduct exploratory data analysis.
• Code solutions following typical workflow; data extraction, cleansing, feature engineering, exploratory data analysis, model selection/creation, hyper-parameter tuning, model interpretation, model retraining, business process and/or system implementations, high level proof of concept and trials, visualization, deployment to production, post deployment ML ops monitoring/diagnosis/resolutions.
• Build, evaluate, and optimize machine learning models through hyperparameter tuning.
• Implement models into production, continuously monitor their performance, and ensure they remain explainable and reliable to minimize model decay.
• Develop custom Machine Learning (ML) solutions and utilize concepts like mlflow to log metrics.
• Create visualizations and reports for stakeholders while working closely with cross-functional teams to align efforts with business objectives.
• Develop and implement generative AI models, focusing on creating new content or augmenting existing data, including understanding GANs, VAEs, Transformers, prompt engineering, RAG, text and image generation tools.

Requirements

• Proficiency in at least one data science programming language (Python, R, Scala, etc.).
• Expertise with modern ML packages and libraries such as Spark, SciKitLearn, Pandas, PyTorch, TidyVerse, Tensorflow, Keras, Shiny, and/or AutoML tools.
• Experience with building, evaluating, and deploying machine learning models, including hyperparameter tuning and model monitoring.
• Knowledge of AI workflows including data extraction, cleansing, feature engineering, exploratory data analysis, model creation, and retraining.
• Familiarity with interactive development environments such as Databricks or Visual Studio Code.
• Understanding of various algorithm categories including Supervised Learning, Unsupervised Learning, Deep Learning, NLP, Computer Vision, Reinforcement Learning, and Knowledge Graphs.
• Experience with generative AI models such as GANs, VAEs, Transformers, and tools like GPT-3/4, DALL-E, and Stable Diffusion.
• Strong analytical skills and strategic thinking in data design and use.
• Ability to work independently with minimal oversight and influence senior leadership.

Nice to Have

• Experience with cloud platforms and tools like Snowflake and Databricks.
• Knowledge of MLflow for experiment tracking.
• Experience with retrieval-augmented generation (RAG) and prompt engineering.
• Familiarity with AI model fine-tuning and agentic architecture.

Qualifications

• Relevant experience in data science, machine learning, or related fields.
• Educational background in a technical or quantitative discipline (implied by the role, though not explicitly stated).

Benefits & Perks

• Medical/Dental/Vision coverage
• 401(k) plan
• Tuition reimbursement program
• Paid Time Off and Holidays (at least 23 days of vacation each year and 9 company-designated holidays)
• Paid Parental Leave
• Paid Caregiver Leave
• Additional sick leave beyond legal requirements
• Adoption Reimbursement
• Disability Benefits (short term and long term)
• Life and Accidental Death Insurance
• Supplemental benefit programs: critical illness/accident hospital indemnity/group legal
• Employee Assistance Programs (EAP)
• Extensive employee wellness programs
• Employee discounts on AT&T products and services