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Job Views:  
170
Applications:  72
Recruiter Actions:  0

Posted in

IT & Systems

Job Code

1642280

Description:



- The ideal candidate is passionate about learning, data, scale, and agility.

- You are a strong collaborator who can extract valuable insights from highly complex datasets, ask the right questions, and find the right answers.

- You thrive in fast-moving environments and excel at turning raw data into actionable intelligence that drives business impact.

Responsibilities:



- Analyze raw data: Assess data quality, perform cleansing, and structure datasets for downstream processing.

- Design scalable predictive models: Develop accurate and scalable algorithms using machine learning, statistical modeling, clustering, and classification techniques.

- Collaborate with cross-functional teams: Work closely with engineering teams to transition analytical prototypes into production-grade solutions.

- Generate actionable insights: Translate data findings into meaningful recommendations that drive business improvements and strategic decisions.

Qualifications:



Education:



- Bachelors degree or equivalent experience in a quantitative field (Statistics, Mathematics, Computer Science, Engineering, etc.

Experience:



- 3-5 years of experience in quantitative analytics, data modeling, or related roles.

Technical Expertise:



- Strong knowledge of predictive modeling, machine-learning algorithms, clustering, and classification techniques.

- Proficiency in at least one programming language such as Python, C, C++, Java, or SQL.

- Familiarity with Big Data technologies like Cassandra, Hadoop, and Spark.

- Experience with data visualization tools such as Tableau.

Soft Skills:



- Strong analytical thinking and problem-solving skills.

- Ability to work collaboratively in agile, cross-functional teams.

- Excellent communication skills with the ability to convert complex findings into clear insights


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Job Views:  
170
Applications:  72
Recruiter Actions:  0

Posted in

IT & Systems

Job Code

1642280

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