
Principal Data Scientist
3 weeks ago
Key Responsibilities :
- Manage end-to-end delivery of data science projects.
- Develop and validate predictive models using analytical tools
- Collaborate with BD team, product managers, data engineers, and other teams to deploy analytical solutions.
- Support internal teams in developing in-house products / analytical solutions
- Build models for descriptive and predictive analytics, explore natural language understanding, conversational AI, forecasting and automation.
- Provide expertise in data wrangling, exploratory data analysis and feature engineering with large data sets.
- Support the development of proofs of concept to demonstrate the application of analytical capabilities in
solving customer problems in collaboration with product and other development teams.
- Align the technology solution with a business :
- 8-10 years of experience in related data science fields.
- Preferred : Masters degree in Computer Science, Mathematics, Statistics, or other quantitative fields.
- Familiarity with Agile methodologies and software development project management.
- Excellent written and verbal communication skills, with experience presenting complex technical information to non-technical audiences.
- Demonstrated flexibility, creativity, and patience in adapting to new situations and changing needs.
- Strong business :
- Proven skills in translating analytics output to actionable recommendations and delivering the same to key stakeholders.- Experience with extracting and aggregating data from large datasets.
- Experience in understanding and analyzing data using statistical software (e.g., Python, R, SQL, PySpark or others).
- Competence in Excel, PowerPoint, and BI tools such as Tableau, Power BI, etc.
- Assess the effectiveness and accuracy of new data sources and data gathering techniques.
- Develop custom data models and algorithms to apply to datasets.
- Use predictive modeling to increase and optimize customer experiences, revenue generation, data insights, and other business outcomes.
- Develop processes and tools to monitor and analyze model performance and data accuracy.
- Partner with key stakeholders/clients to understand the problem statement and convince them with data.
- Extract and understand data to form an opinion on how to best help our partners and derive relevant insights.
- Develop visualizations to make complex analyses accessible to a broad audience.
- Leadership and Stakeholder Management.
- High attention to detail and quality.
- Highly adaptive, comfortable working within a complex environment.
(ref:hirist.tech)-
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