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Senior Data Scientist (Canada)

Posted 18 days agoViewed

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💎 Seniority level: Senior, 5+ years

📍 Location: Canada

🔍 Industry: Advanced analytics consulting

🏢 Company: Tiger Analytics👥 1001-5000AdvertisingConsultingBig DataNewsMachine LearningAnalytics

🗣️ Languages: English

⏳ Experience: 5+ years

🪄 Skills: Machine LearningData scienceData visualization

Requirements:
  • Bachelor's Degree in Data Science, Computer Science, or related field.
  • 5+ years of Data Science and Machine Learning experience required.
  • Proficiency in R and R Shiny.
  • Proficiency with Machine Learning concepts and modeling techniques to solve problems such as clustering, classification, regression, anomaly detection, simulation and optimization problems on large scale data sets.
  • Ability to implement ML best practices for the entire Data Science lifecycle.
  • Ability to apply various analytical models to business use cases (NLP, Supervised, Un-Supervised, Neural Nets, etc.).
  • Exceptional communication and collaboration skills to understand business partner needs and deliver solutions.
  • Bias for action, with the ability to deliver outstanding results through task prioritization and time management.
  • Experience with data visualization tools — Tableau, R Shiny, etc. preferred.
Responsibilities:
  • Collaborate with business partners to develop innovative solutions to meet objectives utilizing cutting edge techniques and tools.
  • Effectively communicate the analytics approach and how it will meet and address objectives to business partners.
  • Advocate and educate on the value of data-driven decision making; focus on the 'how and why' of solutioning.
  • Lead analytic approaches; integrate solutions collaboratively into applications and tools with data engineers, business leads, analysts and developers.
  • Create repeatable, interpretable, dynamic and scalable models that are seamlessly incorporated into analytic data products.
  • Engineer features by using your business acumen to find new ways to combine disparate internal and external data sources.
  • Share your passion for Data Science with the broader enterprise community; identify and develop long-term processes, frameworks, tools, methods and standards.
  • Collaborate, coach, and learn with a growing team of experienced Data Scientists.
  • Stay connected with external sources of ideas through conferences and community engagements.
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