Engineering Data
Resume:
•
LIKITHA RAVIKUMAR
E ****************@*****.***
CAREER OBJECTIVE
Recent industrial engineering management graduate seeking a challenging position to leverage Coding and Data Science. An entry-level industrial engineer who takes pride in building models that translate data points into business insights. I have secured GPA of 7.83 in my bachelors EDUCATION
R.V College of Engineering- June’19
Bachelor of Engineering in industrial
engineering management
Skills
Industrial- Statistics, SQC,
project planning, supply chain,
cad, optimization techniques,
CIM, simulations, manufacturing
process.
Data Analysis Tools: SQL,
Advanced
Excel, Tableau, Statistics, Project
Management, MS office suite.
Database: SQL, DBMS, MS Excel,
MS Access, SAS
Languages: Python, R
OS: Windows
CERTIFICATION
• NPTEL Certification: Principles
of human resource management
and Database management
system by IIT Kharagpur.
• Completed genpact data science
prodegree by Imarticus.
Conference
• International conference trends
in industrial and value
engineering, business and social
and value engineering, business
and social interaction for
presentation of paper on trends
in interaction for presentation of
paper on trends in industry 4.0.
Extracurricular
• Volunteer in blood donation
camp.
• Volunteer in environment day
activities.
• Part of nss and college throw
ball team.
• Coordinator of fine arts in
fest.
Langagues
• English, Telugu, Hindi, Kannada
PROJECTS
Predictive models for loan sanction using credit risk evaluation in python- Data analysis in banking sector.
• For the data provided from (2007-2015) using machine learning algorithms logistic regression and random forest data was pre-processed, models was developed and evaluated and tested.
• The logistic model developed had 95% accuracy and 38% precision.
• The random forest model had 75% accuracy and 40% precision.
• The observed expected loss values are anywhere between 2 percent and 10 percent depending on this exposure.
• Algorithms used- Logistic regression, Random forest Design and development of intelligent prototype for quality improvement in garment manufacturing unit.
• With the help of the help garment defects pictures were collected.
• Designed an Automated fabric defect detector for the company that automatically detects the defects using Neural networks and Fuzzy-C logic and thus, helping the company move towards automation.
• The prototype of the model with accuracy of 50% was developed.
• Tools-Excel, MINITAB, MATLAB
• Techniques used – Neural networks and Fuzzy-C logic Experience
Predictive models for loan sanction using credit risk evaluation in python- Data analysis in banking sector.
• For the data provided from (2007-2015) using machine learning algorithms logistic regression and random forest data was preprocessed, models was developed and evaluated and tested.
• The logistic model developed had 95% accuracy and 38% precision.
• The random forest model had 75% accuracy and 40% precision.
• The observed expected loss values are anywhere between 2 percent and 10 percent depending on this exposure.
• Algorithms used- Logistic regression, Random forest Research associate (Aug. 2019-sep 2019)
Unicorn mark
• Performed business research on R&D operating models at India-based GCCs for EY.
• Collected and analyzed information on Product Portfolio, Product Engineering Job Roles, Skillsets and Leadership Pyramids across R&D GCCs.
• Performed extensive secondary research using 'targeted search string design and query' on LinkedIn. Tools – LinkedIn, Excel Quality Control associate (Jan 2019- June 2019)
Aditya Birla fashion and retail
• Used six sigma tools to understand the process and identify the causes and effect of the defects.
• Used advanced excel to sort the defects from May'18-Jan'19 and reduced defects by 20%
• Developed an automatic fabric defect detection using neural networks.
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