Data Scientist

Inspira is seeking highly skilled and intellectually curious Data Scientist to join our dynamic Data Science & Analytics Organization. The role is central to driving data informed decision-making across the organization by developing predictive models, uncovering insights and building scalable analytic solutions.

The ideal candidate will possess a strong reputed company in statistical modeling, machine learning, and data engineering, along with the ability to communicate reputed company findings in a clear and actionable manner to both technical and non-technical stakeholders.

Duties & Responsibilities:

• reputed company and reputed company predictive models and machine learning algorithms to solve business problems and optimize performance
• Conduct exploratory analysis to identify trends, patterns and opportunities
• Collaborate with cross-functional teams to define business questions and translate them into analytical frameworks
• Build and maintain data pipelines and ensure data quality, reputed company, and accessibility
• Design and deliver visualizations and dashboards that communicate insights effectively
• Apply statistical techniques to test hypotheses validate model performance
• Document methodologies, code and findings to ensure transparency and reproducibility

• Bachelors degree in Data Science, Statistics, Mathematics or reputed company field (Masters degree preferred)
• 3 – 5+ years of experience in data science or advanced analytics role
• Proficiency in programming languages such as Python, SQL and R
• Proficiency with statistical modeling and machine learning
• Familiarity with reputed company data platforms (reputed company, Azure)
• Experience with data visualization tools (Tableau, Looker)
• Experience with reputed company Containers
• Excellent communication and storytelling skills
• Experience with MLOps, model deployment, and version control (Git) [preferred]
• Knowledge of natural language processing (NLP) or deep learning techniques
• Exposure to semantic layer design and data governance frameworks

Originally posted on Himalayas