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ShyftLabs helps enterprises turn data and artificial intelligence into practical business capabilities. Its work spans data and AI consulting, rapid prototyping, solution delivery, platform scaling, and ongoing operations, supporting organizations from early strategy through production deployment. The company develops secure data infrastructure, cloud platform services, analytics, and AI solutions designed to integrate with existing workflows and improve operational performance. ShyftLabs partners with clients across the full lifecycle of their data and AI initiatives, tailoring implementations to their transformation goals.
Data Scientist (Hybrid, India)
ShyftLabs is seeking a Data Scientist to develop predictive models, run experiments, and build data pipelines. The role turns complex analysis into recommendations that inform product and business decisions.
Description
- Develop and validate machine learning models for forecasting, segmentation, and recommendations
- Work with product and engineering teams to frame business questions as analytical problems
- Explore and analyze large datasets to identify meaningful patterns
- Plan and assess A/B tests and other experiments
- Create and maintain data pipelines in collaboration with data engineering
- Present insights through reports, dashboards, and presentations
- Track deployed model performance and drift, improving models when needed
- Help establish strong practices for experimentation, model evaluation, and data quality
- Transform company data into actionable insights and intelligent products
Requirements
- At least three years of experience in data science, applied machine learning, or quantitative analytics
- Advanced working knowledge of Python, including Pandas, NumPy, and Scikit-learn, plus SQL
- Strong understanding of statistics and experiment design, including A/B testing
- Practical experience developing and evaluating machine learning models for real-world applications
- Clear communication skills for explaining technical results to non-technical audiences
- Experience handling large datasets and producing efficient, production-ready analysis code
- Familiarity with Matplotlib, Seaborn, Tableau, or comparable data visualization tools
- Python
- Pandas
- NumPy
- Scikit-learn
- SQL
- Statistics and experiment design, including A/B testing
- Machine learning model development
Benefits
- Competitive compensation with performance-based bonuses
- Flexible working hours with remote and hybrid work options
- Health insurance and wellness benefits
- Budget for learning and professional development
- Collaborative setting where analytical work directly informs decisions
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