Application Development Lead (AL/ML)

The Applications Development Technology Lead Analyst is a senior level position responsible for establishing and implementing new or revised application systems and programs in coordination with the Technology team. The overall objective of this role is to lead applications systems analysis and programming activities.
Responsibilities:

• Lead the design and execution of complex data analysis and AI/ML initiatives across large, structured, and unstructured datasets.
• Develop and deploy predictive, classification, clustering, and forecasting models to support business strategy and risk management.
• Partner with business stakeholders to translate requirements into analytical and machine learning solutions.
• Design and implement feature engineering pipelines and model evaluation frameworks.
• Collaborate with Data Engineering teams to ensure scalable data pipelines and ML-ready datasets.
• Operationalize machine learning models through production deployment and monitoring (MLOps practices).
• Analyze trends, anomalies, and behavioral patterns using statistical and machine learning techniques.
• Ensure model governance, explainability, fairness, and compliance with regulatory requirements.
• Automate analytics workflows and implement scalable AI-driven solutions.
• Present analytical findings and model insights to senior leadership and cross-functional teams.
• Mentor junior analysts and data scientists on advanced analytics and ML best practices.
• Drive continuous improvement in analytical methodologies, model performance, and reporting standards.
• Influence strategic decisions through data science and AI-powered insights.
• Manage multiple priorities in a fast-paced, highly regulated environment.

Recommended Qualifications:

• 8-12 years of relevant experience in Data Analytics, Data Science, or Advanced Analytics roles.
• Extensive experience system analysis and in programming of software applications
• Foundation in Machine Learning and Deep Learning:
  • Solid understanding of classical ML algorithms (e.g., regression, classification, clustering).
  • Expertise in deep learning architectures (e.g., CNNs, RNNs, LSTMs, Transformers).
  • Proficiency in generative models such as GANs (Generative Adversarial Networks), VAEs (Variational Autoencoders), and Diffusion Models.
• Natural Language Processing (NLP):
  • Strong background in NLP concepts and techniques, including text pre-processing, word embeddings, and language modeling.
  • Hands-on experience with large language models (LLMs) like GPT, BERT, and T5.
  • Familiarity with fine-tuning, prompt engineering, and evaluating LLMs.
• Programming and Software Engineering:
  • Proficiency in Python and relevant libraries (e.g., TensorFlow, PyTorch, Keras, scikit-learn, Hugging Face).
  • Strong software development skills, including version control (Git), testing, and CI/CD.
  • Experience with MLOps principles and tools for deploying, monitoring, and maintaining ML models in production.
• Data Engineering:
  • Experience with data pipelines, ETL processes, and data warehousing.
  • Knowledge of big data technologies (e.g., Spark, Hadoop).
• Cloud Computing:
  • Hands-on experience with cloud platforms like AWS, Azure, or GCP.
  • Familiarity with cloud-based ML services (e.g., Amazon SageMaker, Azure Machine Learning, Google AI Platform).

Education:

• Bachelor’s degree/University degree or equivalent experience
• Master’s degree preferred

This job description provides a high-level review of the types of work performed. Other job-related duties may be assigned as required.

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