What are the responsibilities and job description for the DATA SCIENTIST position at C-Vision Inc.?
Data Scientist – Applied Analytics & Insights
(Business-Focused | Azure Databricks Power BI | MVP-Oriented)
Role Summary
We are seeking a business-oriented Data Scientist to bridge the gap between data engineering and analytics leadership by transforming high-quality data into actionable insights, predictive models, and decision-support tools.
This role focuses on applied analytics and real-world impact—working closely with business stakeholders to explore data, build models, and deliver insights that drive measurable outcomes. The Data Scientist will rapidly develop MVP analytics solutions, validate value, and iterate toward scalable data products.
The ideal candidate combines strong analytical and modeling skills (Python, SQL) with the ability to translate business problems into data-driven solutions, without over-engineering.
Where This Role Fits
- Works with Data Engineers → consumes curated datasets (not building pipelines from scratch)
- Partners with Delivery Lead → helps translate business problems into analytical solutions
- Serves the Business → delivers insights, models, and decision tools
Key Responsibilities
Applied Analytics & Insight Generation
- Analyze complex datasets to identify trends, patterns, and business opportunities
- Develop descriptive, diagnostic, and predictive analytics to support decision-making
- Translate business questions into analytical approaches and hypotheses
- Deliver insights in a clear, actionable format tied to business KPIs
Model Development & Experimentation
- Build and deploy predictive models (forecasting, classification, segmentation, etc.)
- Use Python (pandas, scikit-learn, PySpark) for modeling and analysis
- Design and run experiments (A/B testing, scenario analysis)
- Validate models and quantify business impact
MVP Analytics Delivery
- Rapidly develop minimum viable analytical solutions to test business value
- Iterate based on stakeholder feedback and evolving requirements
- Balance speed with rigor—avoid over-engineering early solutions
- Partner with engineers to productionize high-value use cases
Data Exploration & Preparation
- Query and manipulate data using advanced SQL
- Work with curated datasets in Databricks / Azure Data Lake (Silver/Gold layers)
- Perform feature engineering and dataset preparation for modeling
- Identify data quality issues and collaborate with engineers to resolve them
Visualization & Communication
- Develop Power BI dashboards and visualizations to communicate insights
- Present findings to business stakeholders in clear, non-technical language
- Tell compelling data stories that drive decisions and action
- Support adoption of analytical outputs within business workflows
Collaboration & Business Partnership
- Work closely with business stakeholders to understand needs and priorities
- Partner with Data Engineers and BI developers to align on data and outputs
- Contribute to defining high-value use cases and analytics roadmap
- Act as a translator between data and business, but without full ownership of delivery
Required Qualifications
- 4–8 years of experience in data science, analytics, or applied data roles
- Strong proficiency in:
- SQL (advanced querying, transformations)
- Python (pandas, NumPy, scikit-learn or similar)
- Experience building predictive or statistical models
- Experience working with large datasets in cloud environments (Databricks, Spark, or similar)
- Experience with data visualization tools (Power BI preferred)
- Proven ability to translate business problems into analytical solutions
- Strong understanding of data modeling concepts and analytical datasets
Preferred Qualifications
- Experience with Azure data stack (ADF, Databricks, ADLS)
- Experience in ERP, manufacturing, or operational analytics environments
- Familiarity with Medallion architecture (Bronze/Silver/Gold)
- Experience with A/B testing, experimentation frameworks
- Exposure to ML deployment or MLOps concepts
- Experience working in Agile or iterative delivery environments
Technical Skills
- SQL (T-SQL / Spark SQL)
- Python (pandas, scikit-learn, PySpark)
- Databricks / Spark
- Power BI (data modeling, visualization)
- Statistical modeling & machine learning basics
- Data wrangling and feature engineering
Business & Soft Skills
- Strong problem-solving and analytical thinking
- Ability to connect data insights to business outcomes
- Clear communication and data storytelling
- Comfort working in ambiguous, fast-paced environments
- Bias for action and iterative delivery
What Success Looks Like
- Delivery of high-impact analytical insights that influence decisions
- Rapid development of MVP models and analytics solutions
- Strong collaboration with engineering and business teams
- Measurable impact on efficiency, revenue, or decision quality
- High adoption of analytical outputs (dashboards, models, insights)