Senior Data Scientist SME & AI

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

Senior Data Scientist Subject Matter Expert (SME)

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.