Data Scientist/ Senior Data Scientist

Eucloid Data Solutions; Gurugram District, Chennai District; On-site; INR 1,200,000 - 1,800,000; Full time

Job summary

Eucloid Data Solutions is seeking a Data Scientist/Senior Data Scientist to drive end-to-end data science initiatives. You will analyze structured and unstructured data, build ML models, and deploy solutions that deliver actionable business insights.

You will collaborate with data engineers, translate results for stakeholders, and contribute to data-driven client tools while maintaining governance and reproducibility in production environments.

Qualifications

• Bachelor's or Master's degree in data science, computer science, statistics or related field.
• 5+ years hands-on experience in data science, ML, or analytics.
• Proficiency in Python or R for analysis and modeling.
• Strong SQL skills for data extraction and manipulation.

Responsibilities

• Lead data science workstreams: analyze datasets, build and deploy ML models.
• Collaborate with data engineers to design scalable data pipelines.
• Translate complex findings into actionable recommendations for stakeholders.
• Develop data-driven tools and dashboards for clients.

Skills

Analytical thinking

Communication

Agile experience

Cross-functional collaboration

Model deployment

Problem solving

Education

Bachelor's or Master's in data science/CS/Statistics

Tools

Python

R

SQL

Tableau/Power BI/Looker

Pandas/NumPy/Scikit-learn

TensorFlow

Databricks

Git

Job description

Job Description

We are looking for a high-energy individual for the role of Data Scientist /Senior Data Scientist at Eucloid. The candidate is expected to lead following work-streams:

• Analyze structured and unstructured datasets to identify actionable insights and trends.
• Develop and deploy machine learning models and statistical solutions for business challenges.
• Apply statistical techniques including hypothesis testing, probability modeling, and model evaluation metrics (ROC-AUC, Precision/Recall, RMSE, etc.) to ensure robust solutions.
• Collaborate with data engineers to design scalable data pipelines and architectures.
• Translate complex data findings into clear, actionable recommendations for stakeholders.
• Contribute to building data-driven tools and dashboards for clients.
• Support deployment, monitoring, and performance tracking of models in production environments.
• Maintain documentation of methodologies, experiments, and results, ensuring reproducibility and governance.
• Work with version control systems (e.g., Git) and ML lifecycle tools to manage model development and deployment workflows.
• Stay updated on emerging trends in AI/ML and apply learnings to projects.

Qualifications

Academic Background

• Bachelors or masters degree in data science, Computer Science, Statistics or a related field.

Technical Expertise

• 35 years of hands-on experience in data science, machine learning, or analytics.
• Proficiency in Python (e.g., Pandas, NumPy, Scikit-learn) or R for data analysis and modeling.
• Strong SQL skills for data extraction and manipulation.
• Solid understanding of statistics, probability, and model evaluation techniques.
• Experience with machine learning algorithms including regression, classification, clustering, and tree-based models
• Experience in deploying Production level ML solutions
• Familiarity with ML frameworks (e.g., Scikit-learn, TensorFlow)
• Familiarity with MLOps would be preferred
• Experience with data visualization tools (Tableau, Power BI, Looker).
• Experience with cloud platforms like Databricks preferred.
• Basic understanding of deploying models and working with large-scale data platforms.

Skills

• Strong problem-solving mindset and ability to thrive in agile environments.
• Excellent communication skills to convey technical concepts to non-technical audiences.
• Collaborative spirit, with experience working in cross-functional teams.
• Ability to translate business requirements into analytical solutions and actionable insights.
• Experience working in production environments with exposure to monitoring and maintaining deployed models.