HEMANSHU

GUPTA

PERSONAL PROFILE GET IN CONTACT

AREAS OF INTEREST

Mobile: +47 - 4761 4081

Oslo, Norway

A mechanical engineer adol8y@r.postjobfree.com

working in subsea oil and gas

industry for the past 13

years. I have keen interest in

data science and use it to

solve business problems

Data science

Working knowledge of

Python

Classical Machine Learning

Algorithms, supervised &

Unsupervised

Studying Deep Learning,

Neural networks

Microsoft Azure platform

Microsoft Excel and Visual

Basic programming

Microsoft Power BI

SQL (to query database)

COURSES

Microsoft Azure & AWS

Foundation courses on Udacity

Machine Learning Course from

Coursera by Professor Nig

Machine Learning Introductory

Course on Udacity analyzing

Enron dataset

Data analytics courses on

Datacamp

WORK EXPERIENCE

Generated visualizations for the pipeline stalks production data at fabrication yard using python and hence alerted management for critical components delivery plan. Delivery plan (air/ sea freight) was modified to suit production rate (cost of delivery VS. requirement). Production continued without any delays due to right decision of inventor transport selection.

Using VBA, SQL and Microsoft Flow created automated document list update with latest status to be used in fabrication. With just few clicks

(less than 10 seconds), the document list now updates (from SQL server) with changes highlighted and PDF file is automatically sent to stakeholders. Digitized the lengthy manual process (saving of engineering hours) and at the same time updated/ error free (due to automation, quality enhancement) information was available to all stakeholders which was used at fabrication yards.

Using power apps, made customized inventory control app to record inventory movements and consumption on daily basis at Evanton spoolbase. This helped to give real time inventory status at fabrication yards and hence future deliveries were planned/ optimized. Connected PowerBI to Amazon Redshift database to extract real time production data. Applied data transformation steps and created live interactive dashboards. These were shared in Microsoft Teams live via Powerbi online service. This helps management to get quick overview of the production status at one screen and helped to get data insights via interactive dashboards.

Worked as a project engineer on several EPCI (Engineering Procurement Construction and Installation) projects and tenders for major clients like Equinor, Maerks Oil, Neptune Energy, AkerBP & SHELL.

Involved in design, fabrication and installation of subsea pipelines. SPECIALIST ENGINEER - RIGID PIPELINES

TECHNIPFMC NORGE AS 2011 - Present Lysaker, Norway D A T A A N A L Y T I C S / M A C H I N E L E A R N I N G Stress analysis of undergound piping network and onshore pipelines Layout of subsea cables and stability checks

Detail engineering of subsea pipelines and risers. PIPELINE ENGINEER

Bilfinger Tebodin 2011 Abu Dhabi, UAE

ENGINEER - SUBSEA PIPELINES

Larsen & Toubro Limited 2008 - 2011 INDIA

OTHER SKILLS

Detail oriented

Excellent problem solver

Take ownership

Continuous learner

Team work

Positive Attitude

Used Microsoft visual basic scripting for automatic filling up of installation tables and linepipes sorting at spoolbases. This produced error free results quickly (saved engineering manhours by automation).

Written procedures for pipeline fabrication and installation. Participated and conducted risk assessments.

SOFTWARES/

PROGRAMMING

LANGUAGES

PYTHON

SCIKIT LEARN, PANDAS,

NUMPY, MATPLOTLIB/

SEABORN

MS EXCEL, POWER POINT

POWER BI

SQL

POWER APPS

LANGUAGE SKILLS

English (Professional)

Norwegian (Intermediate)

EDUCATION

B.Tech. (Mechanical & Automation

Engineering) from Indraprastha

University, Delhi, INDIA with 81%

(2004-2008).

DATA ANALYTICS/ MACHINE LEARNING KNOWLEDGE

Well versed with the following third party packages for performing data analytics (insights) and prediction (using machine learning algorithms) :

NUMPY - Multi dimensional arrays

PANDAS - Dataframes

MATPLOTLIB - Visualization

SEABORN - Visualization, relational and category plots SCIKIT LEARN - Machine learning algorithms

STATSMODELS - Performing time series prediction

Import data into python from various sources like flat files, text files, MS excel files, relational databases and performing data analysis and visualizing data using Matplotlib/ Seaborn.

Time series analysis (correlation, autocorrelation, working with dates and times in python)

Exploratory data analysis like calculating Pearson correlation coefficients, scatter plots, plotting ECDF( Empirical cumulative distribution function), histograms, bar plots, etc, to understand data and get insights from it.

Hypothesis test (null hypothesis), setting test statistic value and perform hacker statistics using bootstrap/ permutation to find probabilities/ confidence intervals using python.

PYTHON

1.

2.

3.

4.

5.

6.

AR (Auto Regressive Model)

MA (Moving Average Model)

ARMA (Auto Regressive Moving Average Model)

PowerBI

Import/ Connect data from excel/ cloud database. Define the relationships and create dashboards. DAX and creating measures to find metrics.

Contact this candidate