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.

Education and Experience:

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.

Skills and Knowledge:

Candidate must also possess:

• 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.

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