Senior ML Engineer

Role Overview: As a Senior ML Engineer, you will play a crucial role in analyzing, interpreting, and building ML models to drive informed business decisions. Your expertise in statistical techniques, data mining, and reporting will contribute to optimizing efficiency and quality within our organization. Key Responsibilities: - Interpret and analyze data using statistical techniques to identify trends, patterns, and insights. - Develop and implement databases, data collection systems, and data analytics strategies to optimize statistical efficiency and quality. - Own and lead the project and the team under you. - Acquire data from primary and secondary sources to build models. - Build, train, and deploy models into Production systems. - Clean and filter data by reviewing computer reports, printouts, and performance indicators to identify and correct code problems. - Collaborate with management to prioritize business and information needs. - Identify and define new process improvement opportunities based on data analysis findings. - Act as the primary and sole contact for the project. - Develop and present ongoing reports and dashboards to stakeholders, highlighting key insights and recommendations. - Ability to take ad hoc meetings to support offshore customer queries. - Utilize reporting packages, databases, and programming languages (such as SQL, Python, or R) for data analysis and visualization. - Stay updated with the latest trends and advancements in data analysis techniques and tools. Qualifications Required: - Bachelor's degree in computer science, Statistics, Mathematics, or a related field. A master's degree is a plus. - Minimum of 5 years of proven working experience as an ML Engineer or Data Science Engineer, preferably in a technology or finance-related industry. - Strong technical expertise in data models, database design development, data mining, and segmentation techniques. - Proficiency in reporting packages (e.g., Business Objects), databases (e.g., SQL), and programming languages (e.g., Python, R frameworks). - Knowledge of statistics and experience using statistical packages for analyzing datasets (e.g., Excel, SPSS, SAS). - Exceptional analytical skills with the ability to collect, organize, analyze, and disseminate significant amounts of information with attention to detail and accuracy. - Proficient in querying databases, report writing, and presenting findings effectively to both technical and non-technical stakeholders. - Strong problem-solving abilities and a proactive mindset to identify opportunities for process improvements. - Excellent communication and collaboration skills, with the ability to work effectively in a team-oriented environment. - Experience in building OCR models is an advantage.,