Job Description
The main purpose of the “Specialist, Data Science” position is utilize analytical, statistical, and programming skills to collect, analyze, and interpret large data sets. Use this information to develop data-driven solutions to difficult business challenges. Build future insights through prediction models and forecasting, risk management, compliance, Customer segmentation, Recommendation engines, Customer experience, Fraud detection, Customer Data Management, etc. by using data science strategy.
Job Requirement
Duties and Responsibilities
- Data mining or extracting usable data from valuable data sources into the data warehouse.
- Carrying out preprocessing of structured and unstructured data.
- Data mining to identify the trends and patterns.
- Using accurate data set and variables for analysis, using predictive models for decision-making.
- Using machine learning tools to select features, create and optimize classifiers.
- Developing prediction systems and machine learning algorithms.
- Develop processes and tools to monitor and analyze model performance and data accuracy.
- Present information using data visualization techniques.
- Propose solutions and strategies to tackle business challenges.
- Perform other tasks assigned by direct supervisor or management.
Skills Specifications
- Bachelor’s degree in Computer Science or related fields.
- 2 years working experience as a data analyst or in a related field.
- Knowledge of ETL tools such as Talend, Informatica, or Integrate.io.
- Knowledge of reporting & data visualization software like Tableau, Power BI, Oracle BI, or Qlik.
- Knowledge of programming languages like R, or Python, databases such as SQL Server, Oracle, and Postgres DB.
- Knowledge of distributed data/computing tools like Hadoop, Kafka, and Spark.
- Good applied statistical skills, knowledge of statistical tests, distributions, regression, maximum likelihood estimators, etc.
- Good knowledge of machine learning methods like k-Nearest Neighbors, Naive Bayes, SVM, Decision Forests.
- Knowledge of a variety of machine learning techniques like clustering, regression, simulation, scenario analysis, modeling, clustering, decision trees, neural networks, etc.
- Good mathematical skills to help collect, measure, organize and analyze data
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