Principal Data Scientist

Job description

Job Description

Overview: An expert in their field, applying broad business knowledge and strategic insight surrounding emerging trends and technologies to solve complex problems and drive organizational results.

Leads critical, high-impact projects and designs/implements innovative business strategies.

Works with minimal oversight, frequently consulting senior leadership and influencing executive decisions.

Serves as a mentor and assists others with challenging issues.

Performs very complex data science work, creates new ways of analyzing data, builds very complex business models, and makes recommendations that impact an entire business unit or very large complex sector.

Key Roles and Responsibilities: Typical tasks may include, but are not limited to, the following:

• 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.

Tools can include snowflake and databricks.

• Coding Solutions, Algorithms and Feature Engineering: Create relevant features and conduct exploratory data analysis.

Codes 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.

Can 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.

Generative Models-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.

Agentics-Understanding of agentic architecture, concepts and optimization of solutions.

Prompt Engineering-Crafting effective prompts to guide generative models in producing desired outputs.

Retrieval-Augmented Generation (RAG)-Combining generative models with retrieval systems to enhance performance and relevance.

Text Generation-Proficiency in using models like GPT-3/4 for generating human-like text.

Image Generation-Familiarity with tools like DALL-E and Stable Diffusion for creating images from text descriptions.

Key Skills:

Python, R, Scala, Generative AL, Credit, Modele development, Fraud, tiral analysis, Machine Learning (ML), Snowflake, databricks, Spark, SciKitLearn, Pandas, PyTorch, TidyVerse, Tensorflow, Keras, Shiny, AutoML

Weekly Hours:

Time Type:

Location:

It is the policy of AT&T to provide equal employment opportunity (EEO) to all persons regardless of age, color, national origin, citizenship status, physical or mental disability, race, religion, creed, gender, sex, sexual orientation, gender identity and/or expression, genetic information, marital status, status with regard to public assistance, veteran status, or any other characteristic protected by federal, state or local law.

In addition, AT&T will provide reasonable accommodations for qualified individuals with disabilities.

AT&T is a fair chance employer and does not initiate a background check until an offer is made.

Required Skill Profession

Mathematical Science Occupations