Job Location: Thiruvananthapuram
Role Proficiency:
Under supervision leverage data to arrive at business solutions and creates prototypes of the solution to define analytics roadmap for clients thereby bringing in business to the organization.
Outcomes:
- Work with stakeholders and creates quick prototypes of the solution to define analytics roadmap.
- Work with business to understand business domain and convert business problems into analytics problem
- Create or use existing frameworks to test and validate new models
- Explain models and put results in easy to interpret manner such that non-analytic person can understand
- Turn data into information that can improve current workflow or processes that will help to make better business decisions. Generate business insights to help clients with decision making.
- Adhere to best practices of coding
- Set FAST goals and provide feedback on FAST goals of reportees
Measures of Outcomes:
- Schedule Adherence
- Quality of code and other deliverables
- Number of reusable components developed
- Number of new analytics solutions that got approved for further development
Outputs Expected:
Statistical Techniques:
- Apply statistical techniques like regression
properties of distributions
statistical tests etc. to analyse data.
Machine Learning Techniques:
- Apply machine learning techniques like clustering
decision tree learning
artificial neural networks
etc to streamline data analysis.
Creating advanced algorithms:
- Create advanced algorithms and statistics using regression
simulation
scenario analysis
modelling
etc.
Data Visualization:
- Visualizing/presenting data for stakeholders using: Periscope
Business Objects
D3
ggplot
etc.
Management and Strategy:
- Oversees the activities of analyst personnel and ensures the efficient execution of their duties.
Critical business insights:
- Mines the business’s database in search of critical business insights and communicates findings to the relevant departments.
Code:
- Creating efficient and reusable code meant for the improvement
manipulation
and analysis of data.
Version Control:
- Manages project codebase through a version control tool like git
bitbucket etc.
Predictive analytics:
- Seeks to determine likely outcomes by detecting tendencies in descriptive and diagnostic analysis
Prescriptive analytics:
- Attempts to identify what business action to take
Create Reports:
- Create reports depicting the trends and behaviours from the analysed data
- Training end users on new reports and dashboards.
- Set FAST goals and provide feedback to FAST goals of mentees
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
client universities
Status Reporting:
- Report status of tasks assigned
- Comply to project related reporting standards/process
Solution Architecture:
- Create modular architecture such that it can be reused easily across projects.
Analysis:
- Report trends for anomalies
outliers
trend changes
and opportunities from a given data set
Data Gathering:
- Partner with business and gather relevant data
Modelling:
- Data models and experiments for solving business problems
Stakeholder management:
- Explain findings to both technical and nontechnical stakeholder.
New business development:
- Create solution architecture and quick prototypes to explain analytics roadmap to a business team.
Team Management:
- Increase team productivity by upskilling them technically.
Skill Examples:
- Excellent pattern recognition and predictive modelling skills
- Extensive background in data mining and statistical analysis
- Expertise in machine learning techniques and creating algorithms.
- Ability to work with structured semi-structured and unstructured datasets.
- Ability to learn and implement new Data Science algorithms in a fast turnaround time
- Analytical Skills: Ability to work with large amounts of data: facts figures and number crunching.
- Communication Skills: Communicate effectively with diverse people at various organization levels with the right level of detail.
- Critical Thinking: Data analysts must look at the numbers trends and data and come to new conclusions based on the findings.
- Strong meeting facilitation skills as well as presentation skills.
- Attention to Detail: Making sure to be vigilant in the analysis to come to correct conclusions.
- Mathematical Skills to estimate numerical data.
- Work in a team environment
- Ask proactively for help and offer help proactively
- Break big problem into small components and define the solution architecture.
- Explains complex models (like CNN RNN XGBoost etc.) in a manner which is easy to understand for a nontechnical stakeholder.
- Have good understanding of known data science tools and technology like H20 Neo4j PySpark etc.
Knowledge Examples:
- Programming languages – Java/ Python/ R.
- Web Services – Redshift S3 Spark DigitalOcean etc.
- Statistical and data mining techniques: GLM/Regression Random Forest Boosting Trees text mining social network analysis etc.
- Google Analytics Site Catalyst Coremetrics Adwords Crimson Hexagon Facebook Insights etc.
- Computing Tools – Map/Reduce Hadoop Hive Spark Gurobi MySQL etc.
- Database languages such as SQL NoSQL
- Analytical tools and languages such as SAS & Mahout.
- Practical experience with ETL data processing etc.
- Proficiency in MATLAB.
- Data visualization software such as Tableau or Qlik.
- Proficient in Mathematics and Calculations.
- Spreadsheet tools such as Microsoft Excel or Google Sheets
- DBMS
- Operating Systems and software platforms
- Knowledge about customer domain and about sub domain where problem is solved
- Proficient on atleast 1 version control tool like git bitbucket
- Have experience working with project management tool like Jira
- Data Science based high performance application development knowledge is required.
- NLP and Computer Vision tools and libraries – OpenCV spaCY Transformers Attention models etc.
Additional Comments:
NA
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