Senior Data Scientist SME & AI

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.