Cynet Systems Inc
Pay Range: $55hr - $60hr
Responsibilities
Lead data science and analytics initiatives across core retail functions ike merchandising, pricing, supply chain, customer behavior, marketing, and omnichannel operations.
Collaborate with business leaders to define analytics use cases and translate them into high-impact solutions.
Build, deploy, and manage machine learning models and statistical algorithms for forecasting, segmentation, recommendations, and optimization.
Interpret and present analytical findings in business-friendly terms, driving adoption of insights and models.
Work closely with data engineering teams to ensure data availability, quality, and proper pipeline design.
Lead and mentor junior data scientists and analysts, fostering a culture of experimentation and innovation.
Drive adoption of analytics tools and platforms across the organization (e.g., dashboards, self-service BI, ML platforms).
Primary Skills
Strong expertise in data science, machine learning, and statistical modeling.
Proficiency in tools and languages such as Python, R, SQL, Spark, and Databricks.
Experience with retail analytics use cases such as:
Sales & demand forecasting.
Price & promotion analytics.
Customer segmentation & churn prediction.
Inventory & supply chain optimization.
Assortment & space planning.
Hands-on experience with data visualization tools like Power BI, Tableau, or Looker.
Familiarity with cloud ecosystems (Azure, AWS, or GCP) and MLOps practices is a plus.
Strong business acumen and ability to influence stakeholders with data.
Qualifications
Bachelor’s or Master’s degree in Data Science, Statistics, Computer Science, or a related field.
12 years of experience in analytics or data science, preferably in the retail domain.
Proven track record in leading data science projects from ideation to production.
Experience working in Agile/SAFe environments and collaborating with cross-functional teams.
Certifications in data science, cloud platforms, or analytics tools are a plus.
REQUIRED - ADF, SQL(DataOps), Python, Devops.
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Responsibilities
Lead data science and analytics initiatives across core retail functions ike merchandising, pricing, supply chain, customer behavior, marketing, and omnichannel operations.
Collaborate with business leaders to define analytics use cases and translate them into high-impact solutions.
Build, deploy, and manage machine learning models and statistical algorithms for forecasting, segmentation, recommendations, and optimization.
Interpret and present analytical findings in business-friendly terms, driving adoption of insights and models.
Work closely with data engineering teams to ensure data availability, quality, and proper pipeline design.
Lead and mentor junior data scientists and analysts, fostering a culture of experimentation and innovation.
Drive adoption of analytics tools and platforms across the organization (e.g., dashboards, self-service BI, ML platforms).
Primary Skills
Strong expertise in data science, machine learning, and statistical modeling.
Proficiency in tools and languages such as Python, R, SQL, Spark, and Databricks.
Experience with retail analytics use cases such as:
Sales & demand forecasting.
Price & promotion analytics.
Customer segmentation & churn prediction.
Inventory & supply chain optimization.
Assortment & space planning.
Hands-on experience with data visualization tools like Power BI, Tableau, or Looker.
Familiarity with cloud ecosystems (Azure, AWS, or GCP) and MLOps practices is a plus.
Strong business acumen and ability to influence stakeholders with data.
Qualifications
Bachelor’s or Master’s degree in Data Science, Statistics, Computer Science, or a related field.
12 years of experience in analytics or data science, preferably in the retail domain.
Proven track record in leading data science projects from ideation to production.
Experience working in Agile/SAFe environments and collaborating with cross-functional teams.
Certifications in data science, cloud platforms, or analytics tools are a plus.
REQUIRED - ADF, SQL(DataOps), Python, Devops.
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