Role Proficiency:

Independently interprets data and analyses results using statistical techniques

Outcomes:

      Independently Mine and acquire data from primary and secondary sources and reorganize the data in a format that can be easily read by either a machine or a person; generating insights and helping clients make better decisions.       Develop reports and analysis that effectively communicate trends patterns and predictions using relevant data.       Utilizes historical data sets and planned changes to business models and forecast business trends      Working alongside teams within the business or the management team to establish business needs.       Creates visualizations including dashboards flowcharts and graphs to relay business concepts through visuals to colleagues and other relevant stakeholders.       Set FAST goals

Measures of Outcomes:

      Schedule adherence to tasks       Quality – Errors in data interpretation and Modelling       Number of business processes changed due to vital analysis.       Number of insights generated for business decisions       Number of stakeholder appreciations/escalations       Number of customer appreciations

Outputs Expected:

Data Mining:

Acquiring data from various sources

Reorganizing/Filtering data:

Consider only relevant data from the mined data and convert it into a format which is consistent and analysable.

Analysis:

Use statistical methods to analyse data and generate useful results.

Create Data Models:

Use data to create models that depict trends in the customer base and the consumer population as a whole

Create Reports:

Create reports depicting the trends and behaviours from the analysed data

Document:

Create documentation for own work as well as perform peer review of documentation of others' work

Manage knowledge:

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

Status Reporting:

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

Code:

Create efficient and reusable code. Follows coding best practices.

Code Versioning:

Organize and manage the changes and revisions to code. Use a version control tool like git
bitbucket, etc.

Quality:

Provide quality assurance of imported data working with quality assurance analyst if necessary.

Performance Management:

Set FAST Goals and seek feedback from supervisor

Skill Examples:

      Analytical Skills: Ability to work with large amounts of data: facts figures and number crunching.       Communication Skills: Ability to present findings or translate the data into an understandable document       Critical Thinking: Ability to look at the numbers trends and data; coming up with new conclusions based on the findings.       Attention to Detail: Making sure to be vigilant in the analysis to come with accurate conclusions.       Quantitative skills - knowledge of statistical methods and data analysis software       Presentation Skills - reports and oral presentations to senior colleagues       Mathematical skills to estimate numerical data.       Work in a team environment       Proactively ask for and offer help

Knowledge Examples:

      Proficient in mathematics and calculations       Spreadsheet tools such as Microsoft Excel or Google Sheets       Advanced knowledge of Tableau or PowerBI       SQL       Python       DBMS       Operating Systems and software platforms       Knowledge about customer domain and also sub domain where problem is solved       Code version control e.g. git bitbucket etc

Additional Comments:

Proven experience as a Machine Learning Engineer understanding of data structures, data modeling and software architecture.

Deep knowledge of Math, Probability, Statistics and Algorithms

Ability to write robust code in Python, Java

Familiarity with machine learning frameworks

Excellent communication skills

Ability to work in a team

Outstanding analytical and problem-solving skills