CollegeDekho - Senior Data Scientist

Job Overview - Data Science:

We're searching for a Data Scientist to provide insights derived from studying corporate data to our product, sales, leadership, and marketing teams. The candidate should be skilled at analyzing huge data sets to identify the potential for product and process improvement, as well as applying models to evaluate the effectiveness of various strategies.

The candidate must have extensive knowledge of a variety of data mining/data analysis approaches, data tools, and constructing and executing models, algorithms, and simulations. The applicant must have a track record of using data-driven insights to achieve business results. He/she must be able to collaborate with a variety of stakeholders and functional groups. The ideal applicant will enjoy uncovering solutions hidden in massive datasets.

Responsibilities for Data Scientist:

- Collaborate with stakeholders across the organization to explore ways to use company data to create business solutions.

- To optimize and improve product development, marketing approaches, and commercial strategies, data from company databases are mined and analyzed.

- Examine the efficacy and precision of new data sources and data collection procedures.

- To apply to data sets, create bespoke data models and algorithms.

- Increase and optimize customer experiences, revenue creation, ad targeting, and other company results via predictive modeling.

- Develop an A/B testing framework for the organization and evaluate model quality.

- To implement models and track outcomes, collaborate with various functional teams.

- Develop monitoring and analysis methods and tools for model performance and data accuracy.

Qualifications for Data Scientist:

- Strong problem-solving abilities, focusing on product creation.

- Experience manipulating data and extracting insights from huge data sets using statistical computer languages (R, Python, SLQ, etc.).

- Working with and designing data architectures is a plus.

- Understanding of a variety of machine learning approaches (clustering, decision tree learning, artificial neural networks, and so on) as well as their real-world benefits and limitations.

- Advanced statistical techniques and ideas (regression, characteristics of distributions, statistical tests, suitable application, etc.), as well as application experience, are required.

- GLM/Regression, Random Forest, Boosting, Trees, text mining, social network analysis, and other statistical and data mining approaches.

- Experience with databases and statistical programming languages such as R, Python, SLQ, and others.

- Redshift, S3, Spark, Digital Ocean, and other web services experience.

- Regression, simulation, scenario analysis, modeling, clustering, decision trees, neural networks, and other sophisticated machine learning methods and statistics.

- Experience analyzing data from third-party sources such as Google Analytics, and Ad words, among others.