Data scientist 

Job Details

Category:

Information Technology

Career Level:

Experienced (Non-Managerial)

Experience:

3 Years

Required Qualification:

Graduation

Requires Traveling:

No

Salary:

200000 - 300000 PKR

Salary Type:

Per Month

Total Vacancies:

1

Skills

data scientistorganizationinternaldatabusinessoptimizationproduct developmentmarketingalgorithmspredictivecustomerrevenue generationtestingframeworkfunctionalmonitortoolsperformancesciencetechnologyengineeringmathematicseconomicsanalyticsdata sciencemethodscomputer languagespythondrawlargemachine learningclusteringlearningneural networksconceptsregressionmasterverbal communication

Description

JD for data scientist:

• Work with stakeholders throughout the organization and across entities to identify opportunities for leveraging internal and external data to drive business impact.

• Mine and analyze data from company databases to drive optimization and improvement of product development, marketing techniques and business strategies.

• Assess the effectiveness and accuracy of new data sources and data gathering techniques.

• Develop custom data models and algorithms to apply to data sets.

• Use predictive modelling to increase and optimize customer experiences, revenue generation, ad targeting and other business outcomes.

• Develop company A/B testing framework and test model quality.

• Coordinate with different functional teams to implement models and monitor outcomes.

• Develop processes and tools to monitor and analyze model performance and data accuracy.

What You Will Need

• Bachelor’s degree in STEM (Science, Technology, Engineering, Mathematics), or Economics. Advanced degree in a related topic will be added advantage.

• Minimum years of experience in analytics & data science, applying these methods to solve business problems.

• Experience using statistical computer languages (R, Python, SLQ, etc.) to manipulate data and draw insights from large data sets.

• Experience working with and creating data architectures.

• Knowledge of a variety of machine learning techniques (clustering, decision tree learning, artificial neural networks, etc.) and their real-world advantages/drawbacks

• Knowledge of advanced statistical techniques and concepts of regression, properties of distributions, statistical tests and proper usage

• A drive to learn and master new technologies and techniques.

• Excellent written and verbal communication skills for coordinating across globally-spread teams.