Data Scientist

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):

• 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:
• Language: Strong Proficiency in Python
• 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.