Qualification & Experience:

Minimum of 8 years of experience as a Data Scientist/Engineer with demonstrated expertise in data engineering and cloud computing technologies

Technical Responsibilities

Excellent proficiency in Python, with a strong focus on developing advanced skills
Extensive exposure to NLP and image processing concepts
Proficient in version control systems like Git
In-depth understanding of Azure deployments
Expertise in OCR, ML model training, and transfer learning
Experience working with unstructured data formats such as PDFs, DOCX, and images
Strong familiarity with data science best practices and the ML lifecycle
Strong experience with data pipeline development, ETL processes, and data engineering tools such as Apache Airflow, PySpark, or Databricks
Familiarity with cloud computing platforms like Azure, AWS, or GCP, including services like Azure Data Factory, S3, Lambda, and BigQuery
Tool Exposure: Advanced understanding and hands-on experience with Git, Azure, Python, R programming and data engineering tools such as Snowflake, Databricks, or PySpark
Data mining, cleaning and engineering: Leading the identification and merging of relevant data sources, ensuring data quality, and resolving data inconsistencies
Cloud Solutions Architecture: Designing and deploying scalable data engineering workflows on cloud platforms such as Azure, AWS, or GCP
Data Analysis: Executing complex analyses against business requirements using appropriate tools and technologies
Software Development: Leading the development of reusable, version-controlled code under minimal supervision
Big Data Processing: Developing solutions to handle large-scale data processing using tools like Hadoop, Spark, or Databricks

Principal Duties & Key Responsibilities:

Leading data extraction from multiple sources, including PDFs, images, databases, and APIs
Driving optical character recognition (OCR) processes to digitize data from images
Applying advanced natural language processing (NLP) techniques to understand complex data
Developing and implementing highly accurate statistical models and data engineering pipelines to support critical business decisions and continuously monitor their performance
Designing and managing scalable cloud-based data architectures using Azure, AWS, or GCP services
Collaborating closely with business domain experts to identify and drive key business value drivers
Documenting model design choices, algorithm selection processes, and dependencies
Effectively collaborating in cross-functional teams within the CoE and across the organization
Proactively seeking opportunities to contribute beyond assigned tasks
Required Competencies:
Exceptional communication and interpersonal skills
Proficiency in Microsoft Office 365 applications
Ability to work independently, demonstrate initiative, and provide strategic guidance
Strong networking, communication, and people skills
Outstanding organizational skills with the ability to work independently and as part of a team
Excellent technical writing skills
Effective problem-solving abilities
Flexibility and adaptability to work flexible hours as required

Key competencies / Values:

Client Focus: Tailoring skills and understanding client needs to deliver exceptional results
Excellence: Striving for excellence defined by clients, delivering high-quality work
Trust: Building and retaining trust with clients, colleagues, and partners
Teamwork: Collaborating effectively to achieve collective success
Responsibility: Taking ownership of performance and safety, ensuring accountability
People: Creating an inclusive environment

Senior Consultant Data Science