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. Design, build, and analyze large (e.g., hundreds of Terabytes or higher) and complex data sets while thinking strategically about data use and data design.
• Coding Solutions, Algorithms and Feature Engineering: 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.
• Model Development, Deployment and Optimization: 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 solutions and utilize tools like mlflow for logging metrics.
• Visualization and Collaboration: Create visualizations and reports for stakeholders while working closely with cross-functional teams to align efforts with business objectives. Utilize advanced coding methods to produce visualizations (e.g., ggplot, D3.js).
• Generative AI: Develop and implement generative AI models, focusing on creating new content or augmenting existing data. Understand GANs, VAEs, Transformers, and techniques like fine-tuning, prompt engineering, retrieval-augmented generation, and text/image generation tools.

Requirements

• Proficiency in at least one data science programming language (Python, R, Scala, etc.).
• Experience with modern ML packages and libraries such as Spark, SciKitLearn, Pandas, PyTorch, TensorFlow, Keras, TidyVerse, AutoML tools.
• Ability to build, evaluate, and optimize machine learning models, including hyperparameter tuning and deployment.
• Knowledge of AI workflows including data extraction, cleansing, feature engineering, exploratory data analysis, model creation, and monitoring.
• Familiarity with interactive development environments such as Databricks or Visual Studio Code.
• Understanding of advanced AI concepts including supervised/unsupervised learning, deep learning, computer vision, NLP, reinforcement learning, knowledge graphs, and generative models.
• Strong analytical skills and strategic thinking for designing complex data solutions.
• Excellent collaboration skills to work with cross-functional teams and stakeholders.

Nice to Have

• Experience with tools like Snowflake and Databricks.
• Knowledge of advanced generative AI techniques such as GANs, VAEs, and Transformers.
• Experience with retrieval-augmented generation (RAG) and prompt engineering.
• Familiarity with AI model interpretability and explainability techniques.

Qualifications

• Relevant experience in data science, machine learning, or related fields.
• Educational background in a 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 and 9 holidays annually)
• 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