Specialist I - Data Science

Develop data-driven solutions for difficult business challenges. Utilizing analytical statistical and programming skills to collect analyze and interpret large data sets with supervision.

Outcomes:

1. Work with stakeholders throughout the organization to identify opportunities for leveraging data from our customers to create models that can generate business insights
2. Build predictive models and machine learning algorithms to analyse large amounts of information to discover trends and patterns.
3. Mine and analyse data from company databases to drive optimization and improvement of product development marketing techniques business strategies etc
4. Develop processes and tools to monitor and analyse model performance and data accuracy.
5. Coordinate with different functional teams to implement models and monitor outcomes.
6. Set FAST goals and provide feedback on FAST goals of reportees

Measures of Outcomes:

1. Number of business processes changed due to vital analysis.
2. Models applied to Business Problems
3. Number of Business Intelligent Dashboards developed
4. Number of productivity standards defined for project
5. Number of mandatory trainings completed

Outputs Expected:

Statistical Techniques:

1. Apply statistical techniques for example regression
   properties of distributions
   statistical tests
   etc. to analyse data.

Machine Learning Techniques:

1. Apply machine learning techniques like clustering
   decision tree learning
   artificial neural networks
   etc.
   to streamline data analysis.

Creating advanced algorithms:

1. Create advanced algorithms and statistics using regression
   simulation
   scenario analysis
   modelling
   etc.

Data Visualization:

1. Visualize and present data for stakeholders using: Periscope
   Business Objects
   D3
   ggplot
   etc.

Management and Strategy:

1. Oversee the activities of analyst personnel and ensures the efficient execution of their duties.

Critical business insights:

1. Mines the business s database in search of critical business insights and communicates findings to relevant departments.

Code:

1. Create efficient and reusable code meant for the improvement
   manipulation
   and analysis of data.

Version Control:

1. Manages project codebase through version control tools e. g. git
   bitbucket
   etc.

Prescriptive analytics:

1. Attempts to identify what business course of action to make

Create Reports:

1. Creates reports depicting the trends and behaviours from the analysed data
2. Train end users on new reports and dashboards.

Document:

1. Create documentation for personal work as well as performing a peer review of documentation of others work

Manage knowledge:

1. Consume and contribute to project related documents
   share point
   libraries and client universities

Status Reporting:

1. Report status of tasks assigned
2. Comply with project related reporting standards and process

Skill Examples:

1. Excellent pattern recognition and predictive modelling skills
2. Extensive background in data mining and statistical analysis
3. Expertise in machine learning techniques and creating algorithms.
4. Analytical Skills: Ability to work with large amounts of data: facts figures and number crunching.
5. Communication Skills: Communicate effectively with a diverse population at various organization levels with the right level of detail.
6. Critical Thinking: Data Analysts must look at numbers trends and data and come up with new conclusions based on the findings.
7. Attention to Detail: Making sure to be vigilant in the analysis to come to correct conclusions.
8. Mathematical Skills to estimate numerical data.
9. Work in a team environment
10. Proactively ask for and offer help

Knowledge Examples:

Knowledge Examples

1. Programming languages - Java/ Python/ R / Scala
2. Web Services - Redshift S3 Spark DigitalOcean etc.
3. Statistical and data mining techniques: GLM/Regression Random Forest Boosting Trees text mining social network analysis etc.
4. Google Analytics Site Catalyst Coremetrics Adwords Crimson Hexagon Facebook Insights etc.
5. Computing Tools - Map/Reduce Hadoop Hive Spark Gurobi MySQL etc.
6. Database languages such as SQL NoSQL
7. Analytical tools and languages such as SAS & Mahout.
8. Practical experience with ETL data processing etc.
9. Proficiency in MATLAB.
10. Data visualization software such as Tableau or Qlik.
11. Proficient in mathematics and calculations.
12. Utilization of spreadsheet tools such as Microsoft Excel or Google Sheets
13. DBMS
14. Operating Systems and software platforms
15. Knowledge regarding customer domain and sub domain where problem is solved
16. Proficient in at least one version control tool like git bitbucket
17. Have experience working with project management tool akin to Jira

Scala + Spark + AWS/any cloud Design and deliver scalable web services, APIs, and backend data modules. Experience in writing multithreaded programs running in Scala/Spark. Experience with Git and building tools like Gradle/Maven/SBT. Strong understanding of object-oriented design, data structures, algorithms, profiling, and optimization. Knowledge of top algorithms like sorting, heap/stack, queue, search, etc. Familiarity with test-driven development Thrive in a fast-paced environment, with the ability to deliver code of quality quickly. Attention to detail. Strong communication and collaboration skills. Understand requirements and develop reusable code using design patterns & component architecture and write unit test cases. Collaborate with product management and engineering teams to elicit and understand the requirements and develop solutions. Stay current with the latest tools, technology ideas, and methodologies; share knowledge by clearly articulating results and ideas to key decision-makers.