Job
Description
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. O 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.
