Job description
Role: To develop intelligent algorithms and predictive models for Customer Service specific use- cases. The candidate is expected to apply mathematical, problem-solving, and coding skills to manage machine logs, notification data, extracting valuable insights. combine advanced machine learning techniques with clinical domain knowledge to improve and optimize operational efficiency explore new business opportunities enabled by data driven insights and propose them to the business stakeholders. Strong ability to translate business needs into measurable analytics use cases and success criteria Ability to validate data-driven models in real-world service environments and iterate based on operational feedback Experience prioritizing analytics features based on product roadmap, technical feasibility, and expected business impact Proven ability to collaborate closely with cross-functional stakeholders to align on use case scope, validation criteria, and deployment strategy Experience working with domain experts (e.g. service engineers, clinical specialists) to incorporate domain knowledge into model development and interpretation What are my responsibilities? As a Data Scientist , you are required to: · Maintains network to customers, business experts and other subject matter experts to understand the business data analytics requirements, use cases and identify data analytics driven business opportunities. · Design & develop technical solutions to create meaningful insights for business. · Develop analytics models using AI techniques for business problems, using existing ML models, customizing the models. Develop validation strategies for the same. · Configure and deploy algorithms, select optimal tool and define visualization method/tool to display results · Process, manage, extract and cleanse data to apply Data Analytics in a meaningful way (supportive responsibility). · Determine sustainable processes to support fast growing data volumes and ensuring data quality and data accessibility together with the data architect (supportive responsibility). · Regularly scan the Data Science landscape to stay up to date with latest technologies, techniques, tools, and methods in this field Qualification : Master’s or Ph.D. in Computer Science, Data Science, Statistics, Biomedical Engineering, or related field. The candidate should have done course on the following topics for 1 semester (or equivalent): (1) Linear Algebra, (2) Statistics, (3) Artificial Intelligence, Machine Learning (4) Neural Networks (5) Data structures / Algorithms. Experience level : Minimum 5 years in software development with at least 2 - 3 years hands-on experience in Data Science. Desired Knowledge & Experience : · Good understanding of Statistics, Data analytics, Pattern recognition, Machine learning, Neural networks concepts. · Programming experience: o Language: Strong Proficiency in Python o Libraries : Pandas, NumPy, SciPy : packages, Keras with Tensorflow as backend · Experience in databases, data query languages (SQL), Kusto Query Language, Snowflake. · Experience in developing Predictive, Forecasting models, customizing the models, training, deployment, monitoring. · Experience in Azure cloud-based Data Storage and data analytics environment like (Azure BLOB, AZURE Databricks, Snowflake, Azure Data Factory), PySparc. · Working with data from different sources: § Machine Logs. File formats like Parquet files. § Unstructured data, experience in NLP · Experience in representing data in Graph Formats, usage of tools like Neo4J Experience in creating dashboards, visualizations. · SW engineering skills (CI/CD test driven development, GitHub, etc.). · Knowledge of Agentic AI is additional advantage. Required Soft skills & Other Capabilities : · Analytical ability, Great attention to detail. · Drive and the resilience to try new ideas, if the first ones don't work · Collaborative approach to sharing ideas and finding solutions · Ability to work independently and in a global team environment. · Excellent communication skills, to explain your work to people who don't understand the mechanics behind data science. · Knowledge & experience in healthcare domain is preferred.
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