Application Development Lead

• 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

• 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, VAEs, and Diffusion Models
• Strong background in NLP concepts and techniques
• Hands-on experience with large language models (LLMs) like GPT, BERT, and T5
• Familiarity with fine-tuning, prompt engineering, and evaluating LLMs
• 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
• Experience with data pipelines, ETL processes, and data warehousing
• Knowledge of big data technologies (e.g., Spark, Hadoop)
• 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)
• Bachelor’s degree/University degree or equivalent experience
• Master’s degree preferred

Master’s degree preferred