Hello, I'm Mohammed Zakir.
I’m a ServiceNow Developer with 3 years of experience delivering solutions across ITSM, FSM, and CSM
modules, supported by CSA, CAD, and FSM certifications.
I recently completed an MSc in Data Science, strengthening my expertise in Python, SQL, and Machine Learning
to transform data insights into impactful platform improvements that align technology with business needs.
Through FSM process implementations, I improved field-agent adoption by 20% and reduced SLA resolution time
with smarter workflows and integrations, while fostering stakeholder collaboration to ensure every solution
delivers measurable business value
Experience
OCTOBER 2019 - MARCH 2022
Senior Systems Engineer | Infosys
AUGUST 2022 - DECEMBER 2023
PG DIPLOMA | Data Science | IIIT Bangalore
JULY 2024 - JULY 2025
M.Sc | Data Science | LJMU
Skills
Data Analysis
SQL
Python
Excel
NumPy
Pandas
ServiceNow Development
CSA
FSM
HTML
JavaScript
CSS
Projects
Custom ServiceNow App with Real-Time ML Predictions
Built a custom application in ServiceNow with input forms and tables to collect water quality
parameters and send them to a deployed ML model via REST API.
Fetched and stored real-time potability predictions directly within the platform.
Outcome:
Enabled seamless integration of AI-driven predictions into ServiceNow, demonstrating a working
proof-of-concept
that combined field data capture with real-time model inference for smarter decision-making.
Custom Application Development
REST API
UI Action
Business Rules
ServiceNow FSM Capabilities
Documented the end-to-end Work Order lifecycle and OOB capabilities within ServiceNow Field
Service Management (FSM)
,highlighting key user journeys and platform features through a detailed presentation.
Outcome:
Mapped the complete Work Order journey in ServiceNow FSM, highlighting key out-of-the-box (OOB)
features through a structured presentation.
Enhanced cross-team clarity and accelerated onboarding for new users.
FSM
Business Rules
Automation
Workflows
Water Potability Prediction using ML Models
Machine learning-based solution to predict the potability of water using key chemical and
physical parameters aligned with WHO standards. Multiple models were trained and evaluated, with
Random Forest achieving the best performance, supported by SHAP for interpretability.
Outcome:
Machine learning model to predict water potability based on 22 key physicochemical features
with SHAP used to interpret feature importance.The solution was deployed via a Flask API.
This case study showcases a complete analytics solution for a leading e-commerce platform.
It focuses on reducing inventory costs and improving category performance using data insights
Outcome:
Analyze customer behavior, product categories, and sales performance for a leading e-commerce
platform using data analytics.
Python
NumPy
Pandas
EDA
Certifications
Certified System Administrator (CSA)
Certified Implementation Specialist – FSM
Python for Data Science
Data Analysis with Python
Achievement
Academic Star – Recognized for Top Performance in Data Analytics (Batch of 2023)
Infosys Insta Award for good performance (2021–2022)