Data Scientist (Multiple Positions)

Build rules, algorithms, and machine learning (ML) models to identify and extract valuable business insights/information from raw data and data sources. Analyze large amounts of information to discover trends and patterns in data through probability and statistical analysis and automate the collection processes. Build predictive models and machine-learning algorithms. Present information using data visualization techniques. Develop classification and regression algorithms using machine learning techniques. Propose solutions and strategies to business challenges. Collaborate with cross-functional teams (e.g., data engineers, domain experts) to optimize overall project goals. Design, conduct, and analyze A/B experiments. Define and implement key marketing measurement frameworks, proactively identifying opportunities to improve campaign effectiveness. Develop audience targeting strategies based on data science methodologies to enhance marketing precision and impact. Develop knowledge of marketing strategy and experimentation best practices to expand analytical capability and business impact. Support marketing research by applying advanced analytics to answer critical business questions and uncover market opportunities. Influence stakeholders and drive impact without formal authority by leveraging data-driven insights and strategic recommendations.

Qualifications

Must have a Master's degree or foreign equivalent degree in Computer Science, Engineering (any), Information Systems, Data Science, Analytics, Statistics, Mathematics, Finance or a related quantitative field, and 2 years of related work experience; OR a Bachelor's degree or foreign equivalent degree in Computer Science, Engineering (any), Information Systems, Data Science, Analytics, Statistics, Mathematics, Finance or a related quantitative field, and 5 years of post-bachelor's, progressive related work experience. Of the required experience, must have 2 years of experience in each of the following: Using SQL queries and data visualization tools to gather, process, visualize, and analyze large quantities of data to detect trends and anomalies in support of business opportunity identification; Analyzing large quantities of data to detect anomalies using Python and statistical analysis techniques; Developing machine learning models in Python; Providing data-driven insights and recommendations to mitigate insufficiencies models based on model performance, regulations, and business