Jane Doe

Hi, I'm Arth Akhouri

Data Engineer • Analyst • Model Builder

About Me

I am a data-driven and results-oriented Data Engineer with extensive experience in data pipelines, analytics, visualisations, and data science and machine learning solutions. Skilled in Python, SQL, Databricks, and cloud platforms (GCP, AWS), with a proven track record of automating workflows, optimising reporting pipelines, and building predictive models that deliver actionable insights. Passionate about leveraging AI, NLP, and computer vision to solve complex business problems and enhance operational efficiency.

Whether you're looking to collaborate on a project, discuss work opportunities, or simply have a meaningful conversation, I'm always open to connecting and exchanging ideas.

What Can I Do?

Visualisations

I can present your data in a beautiful manner which is also easy to understand

Analysis

Help you better understand your data by performing thorough analysis of it

Automation

Bored of doing a repetitive task? Let me help write scripts to automate it for you

Model Building

Browse multiple models and help you create and train that model for your task

Work Experience

Data Integration Analyst — Mondelēz International

Sep 2024 – Present

Leading migration of OTIF reporting to Google Cloud with automated Databricks workflows and analytics optimisation, improving scalability, cutting reporting time by 97%, and reducing costs.

  • Leading the migration of On-Time In-Full reporting from Alteryx to Google Cloud Platform, integrating data from SAP and manual sources via Talend pipelines and Aecorsoft, improving scalability and reducing manual errors.
  • Engineered an automated SKU Rationalisation workflow in Databricks with Unity Catalog governance, reducing report generation time by ∼97% (50+ hours → ∼30 minutes) and enabling quicker SKU tracking across supply chain teams.
  • Analysed the use of 30+ analytics products, generating detailed insights about adoptions and recommending discontinuation to optimise efficiency and reduce costs.

Associate Process Manager — eClerx

Jan 2022 – Jun 2023

Developed an end-to-end Databricks ETL and analytics workflow leveraging SQL, Python, and Adobe Analytics to boost website traffic and automate reporting, reducing production time by 90%.

  • Developed an end-to-end ETL pipeline in Databricks to generate a comprehensive dashboard for Traffic Analytics, utilising tools like SQL for data manipulation and Python and Matplotlib for visualisations.
  • Leveraged keyword search data to increase client website traffic, applying insights from Adobe Analytics and custom Python workflows to optimise content performance and user acquisition.
  • Automated Traffic Analytics report generation using Python, cutting production time from 2.5 days to 6 hours.
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Data Science Intern — AVL

Dec 2021 – May 2022

Analysed IIoT time series data using Python and machine learning to optimise energy efficiency, enhance operational insights with interactive visualisations, and establish safety thresholds for machinery.

  • Analysed time series electrical data from 3 IIOT ecosystems to identify inefficiencies and optimise energy consumption using Python and statistical methods.
  • Created 20+ interactive visualisations using Plotly to illustrate individual machine parameters, focusing on energy consumption and operational performance.
  • Employed machine learning techniques to establish threshold safety values for machinery, ensuring maintenance and safety.

Data Science Intern — AkzoNobel

Sep 2021 – Mar 2022

Developed and optimised ML/DL models for colour detection and recipe optimisation, improving colour matching accuracy and reducing base colours required from 4–6 to 2–3 for greater resource efficiency.

  • Collected and integrated large datasets of colour recipes to identify different toners, leveraging data pre-processing techniques.
  • Built and optimised machine learning (ML) and deep learning (DL) models to accurately detect colours based on reflection values, improving colour matching accuracy.
  • Developed ML/DL models to optimise colour recipes, reducing base colours required from 4-6 to 2-3, enhancing resource efficiency in recreating a colour.

Education

M.Sc. Data Science — School of Informatics, The University of Edinburgh

2023 — 2024

Building on top of the knowledge gained in my undergraduate degree and delving into more advanced concepts in the field. Keeping an organised and detail-oriented approach helps in managing all the course load simultaneously helping me grow as in individual. Effectively utilising knowledge from various domains to implement the projects efficiently.

  • Machine & Deep Learning
  • Natural Language Processing
  • Big Data
  • Large Language Models
  • Knowledge Graphs
  • Financial Analytics

B. Tech. Data Science — MPSTME, Narsee Monjee Institute of Management Studies

2018 — 2022

Gained foundational knowledge about the field of data science. Started with preliminary concepts and climbed up to more complex algorithms. Reinforcing the theoretical knowledge gained with practical applications. Worked on various projects across different domains of Data Science.

  • Statistics Methods
  • Data Visualisation
  • Machine & Deep Learning
  • Computer Vision
  • Natural Language Processing
  • Big Data

Projects

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