ShyftLabs
ShyftLabs
ShyftLabs palīdz uzņēmumiem pārvērst datus un mākslīgo intelektu praktiskās uzņēmējdarbības iespējās. Uzņēmuma darbs aptver datu un mākslīgā intelekta konsultācijas, ātru prototipu izstrādi, risinājumu ieviešanu, platformu mērogošanu un nepārtrauktu darbību, atbalstot organizācijas no sākotnējās stratēģijas izstrādes līdz ieviešanai ražošanas vidē. Uzņēmums izstrādā drošu datu infrastruktūru, mākoņplatformu pakalpojumus, analītikas un mākslīgā intelekta risinājumus, kas paredzēti integrācijai esošajos darba procesos un darbības efektivitātes uzlabošanai. ShyftLabs sadarbojas ar klientiem visā viņu datu un mākslīgā intelekta iniciatīvu dzīves ciklā, pielāgojot ieviešanu viņu pārveides mērķiem.

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.

Apraksts

  • 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

Prasības

  • 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

Priekšrocības

  • 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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