Lead Data Scientist

This position requires office presence of a minimum of 5 days per week and is only located in the location(s) posted. No relocation is offered.

Overall Purpose

Translate business problems into actionable insights through a comprehensive workflow involving coding, data extraction, cleansing, feature engineering, exploratory data analysis, model creation and tuning, visualization, and deployment, leveraging statistical analysis, machine learning, and big data technologies to drive informed decision-making and innovation.

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 as technology advances) 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. Coding proficiency required in at least one data science language (Python, R, Scala, etc.), as well as expertise with modern ML packages and libraries (Spark, SciKitLearn, Pandas, PyTorch, TidyVerse, Tensorflow, Keras, Shiny, and/or AutoML tools).
• 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. Ability to develop custom Machine Learning (ML). Highly proficient in the full AI workflow such as (1) data extraction, cleansing, feature engineering, exploratory data analysis, model selection/creation, hyper-parameter tuning, model interpretation, model retraining and (2) uses concepts like mlflow to log metrics. Well-versed in Interactive Development Environments (IDEs) such as Databricks Workspaces or Visual Studio Code. Proficiency in algorithm categories such as Supervised Learning, Unsupervised Learning, Optimization Algorithms, Deep Learning, AI-Computer Vision, Natural Language Processing, Deep Reinforcement Learning, Search Algorithms, and AI-Knowledge Graphs.
• 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, etc.).
• Generative AI: Develop and implement generative AI models, focusing on creating new content or augmenting existing data. Includes understanding of GANs (Generative Adversarial Networks), VAEs (Variational Autoencoders), and Transformers; fine-tuning techniques for adapting pre-trained models to specific tasks using smaller, task-specific datasets; agentic architecture, concepts and optimization of solutions; prompt engineering; retrieval-augmented generation (RAG); text generation (e.g., GPT-3/4); and image generation (e.g., DALL-E, Stable Diffusion).

Job Contribution

An experienced professional, recognized as an expert, creatively resolving complex issues with broad and in-depth knowledge. Leads significant projects with strategic autonomy, influencing executive decisions. Mentors less experienced staff, implements long-term plans impacting the organization, and frequently collaborates with senior leadership.

Education/Experience

• Master's degree (MS/MA) required from an accredited University in a Quantitative field of study such as Data Science, Math, Statistics, Engineering or Physics.
• 5+ years of related experience.
• Certification is required in some areas.

Pay

Lead Data Scientist earn between $160,900 - $270,400. Individual starting salary within this range may depend on geography, experience, expertise, and education/training.

Benefits

• Medical/Dental/Vision coverage
• 401(k) plan
• Tuition reimbursement program
• Paid Time Off and Holidays (based on date of hire, at least 23 days of vacation each year and 9 company-designated holidays)
• Paid Parental Leave
• Paid Caregiver Leave
• Additional sick leave beyond what state and local law require may be available but is unprotected
• 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 up to 50% off on eligible AT&T mobility plans and accessories, AT&T internet (and fiber where available) and AT&T phone