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
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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
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Ability to validate data-driven models in real-world service environments and iterate based on operational feedback
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Experience prioritizing analytics features based on product roadmap, technical feasibility, and expected business impact
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Proven ability to collaborate closely with cross-functional stakeholders to align on use case scope, validation criteria, and deployment strategy
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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.
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
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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.
