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.