Senior Data Scientist SME & AI Architect

🌟 Senior Data Scientist SME & AI Architect (10+ Years Experience) 🧠

We are seeking a highly accomplished and results-oriented Senior Data Scientist Subject Matter Expert (SME) with over 10 years of experience to lead our advanced analytics and AI initiatives. This is a pivotal role requiring deep technical mastery across large-scale Big Data technologies, multi-cloud environments, and cutting-edge specialized AI domains, including Generative AI, Computer Vision, and Natural Language Processing (NLP).

You will be the principal technical leader, driving strategy, setting standards, and delivering high-impact solutions that transform business outcomes.

Key Responsibilities

• AI/ML Strategy & Architecture: Define the technical roadmap and architectural standards for deploying and scaling complex AI systems, particularly those involving Generative AI (GenAI), large language models (LLMs), and specialized models in Computer Vision and NLP.
• Big Data Engineering: Design, build, and optimize high-throughput, distributed data pipelines and features using Apache Spark (Scala) and Hive on massive datasets to support model training and inference.
• Cross-Cloud Execution: Lead the design and deployment of ML models and data infrastructure across multiple major cloud providers (AWS, Azure, and GCP), ensuring portability, scalability, and cost efficiency.
• Specialized Model Development: Lead hands-on development, fine-tuning, and deployment of production-grade models in key specialized areas:
• Computer Vision: Developing and optimizing models for image recognition, object detection, and video analytics.
• NLP: Building sophisticated systems for sentiment analysis, entity extraction, semantic search, and RAG architectures leveraging LLMs.
• Generative AI: Exploring and implementing cutting-edge GenAI techniques for content creation, data augmentation, and innovative product features.
• SME Consulting & Mentorship: Act as the internal authority and consultant, providing technical guidance, architectural review, and mentorship to junior data scientists and engineering teams.
• MLOps & Governance: Establish best practices for MLOps, model monitoring, version control, and model risk governance in a multi-cloud production environment.

Required Skills and Expertise (10+ Years)

1. Big Data and Cloud Mastery

• Programming & Big Data: 10+ years of extensive, hands-on experience with Apache Spark, with strong preference for production development using Scala. Deep expertise with Apache Hive for data querying and management.
• Cloud Proficiency: Demonstrated expertise in deploying and managing data/ML workloads across at least two of the three major cloud platforms: AWS (Sagemaker, EMR, S3), Azure (Azure ML, Synapse Analytics), and GCP (Vertex AI, BigQuery).
• Data Architecture: Expert knowledge of distributed systems, data partitioning, optimization techniques, and data warehousing concepts in a cloud-native context.

2. Advanced AI/ML Specialization

• Generative AI (GenAI) & LLMs: Proven experience with the architecture and implementation of Generative AI solutions, including prompt engineering, fine-tuning, and deploying Large Language Models (LLMs).
• Computer Vision: In-depth knowledge of deep learning frameworks (TensorFlow, PyTorch) and experience with Computer Vision tasks (e.g., CNNs, object detection models like YOLO).
• NLP: Expert practical experience in NLP techniques, including transformer models, embedding generation, and building complex text-based applications.

3. Leadership & Soft Skills

• Technical Leadership: Proven track record of leading complex data science projects from research to production deployment.
• Communication: Exceptional ability to translate complex technical findings into clear, strategic recommendations for technical and executive audiences.
• Mentorship: Experience mentoring and training senior engineers and data scientists.

Education and Certification

• Master’s or Ph.D. in Computer Science, Data Science, Engineering, or a highly quantitative field.
• Relevant professional cloud certifications (e.g., AWS Certified Machine Learning Specialty, Google Cloud Professional Data Engineer) are highly desirable.