Data Science associate engineer
Anicalls
Mumbai, India
4d ago

Anicalls

  • Ability to work on 2-3 projects simultaneously, scope projects, realistically estimate work, and get customer agreement.
  • Apply appropriate statistical analysis and quantitative methods to analyze data and predict future trends and variability, especially in producing and maintaining reliable forecasts.
  • Contribute high-quality work as an individual contributor, in addition to management duties
  • Identify appropriate decision technology techniques to apply to relevant analytic frameworks. Examples of decision technology tools that may be used include optimization, simulation, regression, decision trees, neural networks, cluster analysis, mixed models, etc.
  • Ability to balance accuracy of analysis with needs for rapid response.
  • Perform due diligence to ensure data is obtainable for the analysis required, and the analysis can be completed in the timeline requested.
  • Set up a model and conduct analyses using statistical, predictive or optimization tools.
  • Have a clear understanding of the uses and limitations of statistical applications.
  • Document steps, data sources. Ensure models are easily understandable and maintainable.
  • Drive the collection of new data and the refinement of existing data sources.
  • Possess a proven ability to develop partnerships with technical peers and business partners.
  • Lead project teams during all phases of the development life cycle, including requirements gathering and analysis, design, build, pilot, implementation, and continuous service
  • Lead functional teams to manage the end-to-end process : scope projects, build pipelines and datasets, synthesize results, craft automated dashboards / bots, and communicate an informed recommendation.
  • Manage budget, timeline, resources, and scope through prioritization throughout the course of all assigned projects
  • Lead a high-performing, high-throughput team of talented Data Scientists and business analysts, while driving the professional development goals of the team
  • Where relevant, clean, and structured data to facilitate analysis.
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