S
SANJIR SALSABIL
Machine Learning Engineer
Profile
About you
Summary
Machine Learning Engineer | Ex-Senior Business Analyst at BMO | MSc in Data Science, UofA
Total work experience
5-7 yrs
I can work legally in
Canada
Availability
6 months
Current work sector
Technology(Strategy/Business Analyst)
Languages
English (US)(Fluent), French(Competent)
Toronto, Ontario, Canada
Education
Postgraduate Degree - Masters of Msc in Data Science and Big Data Analytics
Edmonton, Alberta, Canada
3 or 4 yr. Undergraduate degree of Data Science
Kuala Lumpur, Kuala Lumpur, Malaysia
Work experience
Senior Business Analyst, Bank of Montreal
2023 - 2024
Machine Learning Engineer, Minerva Analytics INC., USA
2020 - 2024
Workplace preferences
Ideal workplace
Co-workers
Office meetings
Flexible working hours
Speed of change
Work from home policy
Ideal job
Desired job title
Machine Learning Engineer
Position type
Permanent
Travel
25%
Relocation
Yes
Kaggle profile
Followers
none yet
Skills
Personal data
For how many years have you been writing code and/or programming?
12 years
For how many years have you used machine learning methods?
5 years
Select the title most similar to your current role (or most recent title if retired)
Machine Learning/ MLops Engineer
Select any activities that make up an important part of your role at work: (Select all that apply)
- Analyze, understand and visualize data to influence product or business decisions
- Build and/or run the data infrastructure that my business uses for storing, analyzing, and operationalizing data
- Build and/or run a machine learning service that operationally improves my product or workflows
- Experimentation and iteration to improve existing ML models
- Do research that advances the state of the art of machine learning
Approximately how much money have you spent on machine learning and/or cloud computing services at home or at work in the past 5 years (approximate $USD)?
$100,000 or more ($USD)
Approximately how many times have you used a TPU (tensor processing unit)?
2-5 times
What is the size of the company where you are employed?
10,000 or more employees
Approximately how many individuals are responsible for data science workloads at your place of business?
10-14
Technical skills
Generative AI
Prompt Engineering
Embeddings and Vector Stores/Databases
Generative AI Agents
MLOps for Generative AI
Programming languages
Python
R
SQL
Rust
C++
Java
Javascript
Julia
Bash
MATLAB
PHP
ML algorithms
Linear or Logistic Regression
Decision Trees or Random Forests
Gradient Boosting Machines (xgboost, lightgbm, etc)
Bayesian Approaches
Evolutionary Approaches
Dense Neural Networks (MLPs, etc)
Convolutional Neural Networks
Generative Adversarial Networks
Recurrent Neural Networks
Transformer Networks (BERT, gpt-3, etc)
Autoencoder Networks (DAE, VAE, etc)
Graph Neural Networks
ML frameworks
Scikit-learn
TensorFlow
Keras
PyTorch
Fast.ai
Xgboost
LightGBM
CatBoost
Huggingface
Computer Vision Methods
General purpose image/video tools (PIL, cv2, skimage, etc)
Image segmentation methods (U-Net, Mask R-CNN, etc)
Object detection methods (YOLOv6, RetinaNet, etc)
Image classification and other general purpose networks
(VGG, Inception, ResNet, ResNeXt, NASNet, EfficientNet, etc)
(VGG, Inception, ResNet, ResNeXt, NASNet, EfficientNet, etc)
Vision transformer networks (ViT, DeiT, BiT, BEiT, Swin, etc)
Generative Networks (GAN, VAE, etc)
Natural Language Processing (NLP) Methods
Transformer language models (GPT-3, BERT, XLnet, etc) - General
Transformer language models - pre-training
Transformer language models - fine-tuning
Transformer language models - reinforcement learning from human feedback
Text Embedding Models (BGE, E5, T5, etc.)
Production-Grade ML
ML System Architecture design (Training, Inference, microservices orchestration)
AB testing
Model testing
Investigation of production incidents and Root Cause Analysis
ML OPS best practices
Feedback discussions with end users of the ML systems
Industry experience
Finance and Banking
Algorithmic trading
Fraud detection
Credit scoring
Risk management
Customer service automation
Churn prediction
Technology and Information Services
Advancements in algorithms
Cloud computing
Data analytics
Development of new software and hardware solutions
Other use cases
Healthcare and Biotechnology
Diagnostic imaging
Patient data analysis
Predictive modeling for patient care
Retail and E-commerce
Inventory management
Attribution analysis and modelling
Automotive and Transportation
Traffic management
Route optimization
Predictive maintenance of vehicles
Telecommunications
Network optimization
Customer service automation
Fraud detection