Data Scientist

Responsibilities

• Responsible for overseeing the data scientists' team and data specialists.
• Teach, lead, and counsel colleagues on new techniques or solutions.
• Collaborate with data and software engineers to enable the deployment of sciences and technologies that will scale across the company's ecosystem.
• Responsible for the conception, planning, and prioritizing of data projects.
• Responsible for building analytic systems and predictive models, as well as experimenting with new models and techniques.
• Ensure that data projects align with organizational goals.
  

Requirements

• Master's degree in Computer Science, Operations Research, Econometrics, Statistics, or related technical field.
• 3+ years of experience solving analytical problems using quantitative approaches.
• Experience communicating quantitative analysis results.
• Knowledge of relational databases and SQL.
• Development experience in any scripting language (PHP, Python, Perl, etc. ).
• Knowledge of statistics (e. g., hypothesis testing, regression).
• Experience manipulating data sets through statistical software (ex., R, SAS) or other methods.
  

Must Have

• Programming - Python (Preferred) / R.
• ML Models - i. Regression - Linear, Logistic, Multinomial, Mixed effect.
• Classification - Bagging and Boosting (Random Forest), Decision tree, SVM.
• Clustering - K-Means, hierarchical, DB-Scan.
• Time series - ARIMA, SARIMA, ARIMAX, Holt-Winters, Multi TS (VAR), UCM, Neural Networks (Deep learning), Naive Bayes, Excel, and SQL.
• Dimensionality Reduction - PCA, SVD, etc.
• Optimization Techniques - Linear programming, Gradient Descent, Genetic Algorithm.
• Cloud - Understanding of Azure / AWS offerings, setting up an ML pipeline in the cloud.
  

Good To Have

• Visualization - Tableau / Power BI / Looker / QlikView.
• Data management - HDFS, Spark, Advanced Excel.
• Agile Tools - Azure DevOps, JIRA, Pyspark, Big Data/Hive Database.
• IDE: PyCharm.