The ideal candidate's favorite words are learning, data, scale, and agility. You will leverage your strong collaboration skills and ability to extract valuable insights from highly complex data sets to ask the right questions and find the right answers. Responsibilities - Analyze raw data: assessing quality, cleansing, structuring for downstream processing - Design accurate and scalable prediction algorithms - Collaborate with engineering team to bring analytical prototypes to production - Generate actionable insights for business improvements Qualifications - Bachelor's degree or equivalent experience in a quantitative field (Statistics, Mathematics, Computer Science, Engineering, etc.) - At least 1 - 2 years" of experience in quantitative analytics or data modeling - Deep understanding of predictive modeling, machine-learning, clustering and classification techniques, and algorithms - Fluency in a programming language (Python, C, C++, Java, SQL) - Familiarity with Big Data frameworks and visualization tools (Cassandra, Hadoop, Spark, Tableau),
Role Overview: As a data-driven individual with a passion for learning and agility, you will play a crucial role in leveraging your collaboration skills to extract valuable insights from complex data sets. Your primary focus will be on analyzing raw data, designing scalable prediction algorithms, collaborating with the engineering team, and generating actionable insights for business enhancements. Key Responsibilities: - Analyze raw data by evaluating quality, cleansing, and structuring for downstream processing - Develop accurate and scalable prediction algorithms - Work closely with the engineering team to transition analytical prototypes to production - Create actionable insights to drive business improvements Qualifications: - Bachelor's degree or equivalent experience in a quantitative field such as Statistics, Mathematics, Computer Science, or Engineering - 1-2 years of experience in quantitative analytics or data modeling - Profound knowledge of predictive modeling, machine-learning, clustering, classification techniques, and algorithms - Proficiency in at least one programming language like Python, C, C++, Java, or SQL - Familiarity with Big Data frameworks and visualization tools such as Cassandra, Hadoop, Spark, and Tableau,