Director, Data Science

Fidelity-posted 6 days ago

Full-time • Director

Hybrid • Durham, NC

5,001-10,000 employees

Resume

Match Score

Upload and Match ResumeTrack Jobs with Teal

Position Description : Builds algorithms using programming languages (Python, C++, or Java), Machine Learning (ML) (scikit-learn), and Deep Learning (DL) frameworks ( PyTorch or TensorFlow). Collects requirements and delivers Artificial Intelligence (AI) and ML solutions that drive customer and business value. Creates Web applications employing front-end technologies – React.js. Develops AI models on Cloud platforms – Amazon Web Services (AWS) Sagemaker . Uses visualization dashboard tools for result monitors – Tableau or Qlik Sense. Collaborates closely with the Product Owner to define tasks for upcoming sprints and manages stories, using Jira Tool. Oversees end-to-end AI/ML lifecycle management, including model versioning, data drift monitoring, and MLOps standards, using MLflow , Jenkins, or Amazon SageMaker Pipelines, to ensure scalable and reliable AI solutions in production . Primary Responsibilities: Develops and deploys AI models to address business needs by understanding the business problem , researching possible solutions , and prototyping AI capabilities. Works closely with AI teams, business stakeholders, and deployment teams to ensure alignment with business objectives . Trains and deploys advance DL and Natural Language Processing (NLP) models (RNNs, Seq-to-Seq, BERT, Adversarial Networks, LSTMs, GANs at scale. Performs orchestration of training workflows, inference endpoints, and batch predictions. Performs model evaluation, tuning, and scalability using distributed systems, parallel and multi-threaded programming techniques, and high-performance GPU environments. Supports the operational deployment of AI/ML solutions. Leads and oversees the full AI/ML lifecycle - -data ingestion, model development, training, deployment, and monitoring. Develops and delivers projects involving large-scale multi-dimensional databases and big data technologies, in collaboration with cross-functional teams and enterprise infrastructure. Evaluates and makes decisions around the use of new or existing tools for a project. Analyzes user needs and develops software solutions, applying principles and techniques of computer science, engineering, and mathematical analysis. Researches , designs , and develops computer and network software or specialized utility programs .

Develops and deploys AI models to address business needs by understanding the business problem , researching possible solutions , and prototyping AI capabilities.
Works closely with AI teams, business stakeholders, and deployment teams to ensure alignment with business objectives .
Trains and deploys advance DL and Natural Language Processing (NLP) models (RNNs, Seq-to-Seq, BERT, Adversarial Networks, LSTMs, GANs at scale.
Performs orchestration of training workflows, inference endpoints, and batch predictions.
Performs model evaluation, tuning, and scalability using distributed systems, parallel and multi-threaded programming techniques, and high-performance GPU environments.
Supports the operational deployment of AI/ML solutions.
Leads and oversees the full AI/ML lifecycle - -data ingestion, model development, training, deployment, and monitoring.
Develops and delivers projects involving large-scale multi-dimensional databases and big data technologies, in collaboration with cross-functional teams and enterprise infrastructure.
Evaluates and makes decisions around the use of new or existing tools for a project.
Analyzes user needs and develops software solutions, applying principles and techniques of computer science, engineering, and mathematical analysis.
Researches , designs , and develops computer and network software or specialized utility programs .

Bachelor’s degree in Data Analytics, Computer Science, Engineering, Information Technology, Information Systems, Information Management, Business Administration, or a closely related field (or foreign education equivalent) and six (6) years of experience as a Director, Data Science (or closely related occupation) building algorithms using programming languages (Python, C++, Java, or Spark) and Machine Learning (ML) or Deep Learning (DL) frameworks (scikit-learn, Tensorflow , PyTorch , or Keras ), to deploy applications in a financial services environment .
Or, alternatively, Master’s degree in Data Analytics, Computer Science, Engineering, Information Technology, Information Systems, Information Management, Business Administration, or a closely related field (or foreign education equivalent) and four (4) years of experience as a Director, Data Science (or closely related occupation) building algorithms using programming languages (Python, C++, Java, or Spark) and Machine Learning (ML) or Deep Learning ( DL) frameworks (scikit-learn, Tensorflow , PyTorch , or Keras ), to deploy applications in a financial services environment .
Demonstrated Expertise (“DE”) performing advanced statistical modelling to develop, analyze, and evaluate supervised and unsupervised ML algorithms, using Neural Networks (RNNs (Recurrent Neural Networks), Seq-to-Seq, BERT (Bidirectional Encoder Representations from Transformers), Adversarial Networks, and LSTMs (Long Short-Term Memory)), Feature Selection, Clustering (Uniform Manifold Approximation and Projection (UMAP)), t-distributed Stochastic Neighbor Embedding(T-SNE), marketing attribution models, and treatment control matching using programming languages (Python, C++, or Java), within a financial services environment .
DE launching ML and DL models in online advertising (Clickstream data, Adobe, or Google analytics), Recommender Systems (Bandit algorithms, Bayesian models, NVIDIA Merlin, or Meta DRLM (Deep Learning Recommendation with Multi-Armed Bandits)), and user behavior applications (RNNs, BERT, LSTMs, or GANs (Generative Adversarial Networks)), using Python, C++, or Java to write production-level code and achieve greater performance; prototyping and deploying ML solutions using experimentation design (A/B Testing and Off-policy evaluation) within a financial services environment .
DE writing production-level code to deploy AI solutions, and achieve greater run-time performance and low latency according to a Test-Driven Development (TDD) mindset, using pytest (for unit and integration testing), Onnx Runtime, and Tensor RT (for optimizing model inference); automating build, test, and deployment of Docker-based ML models, using Jenkins and CI/CD pipelines; developing and deploying ML solutions and integrating caching mechanisms and client-server architecture, using Docker-containers in Cloud-based environments on AWS Sagemaker ; building service endpoints using REST API, Flask, or Django; and developing front-end interfaces using ReactJS, within a financial services environment .
DE improving financial planning, advice offerings, and recommendations while liaising with business, product, and engineering stakeholder teams to assess the validity of ML models, using experimentation design; and communicating revenue or cost saving benefits to senior leadership within financial services environment, using business intelligence tools -- Seaborn, Altair, Streamlit , Plotly , Tableau, or Qlik .

Track Jobs with Teal

Job Search Resources

•

AI Resume Builder

•

Director of Data Science Resume Examples

•

Director of Data Science Cover Letter Examples

Director, Data Science

Job Search Resources

Tools

Career Hubs

Guides

Company