Machine Learning Engineer
Summary
1. Developed computer vision based applications such as jewelry detection, visual image similarity based recommendation engine.
2. Developed Natural Language Processing (NLP) applications such as sentiment analysis on customer conversations to improve customer experience, missed sale opportunity model using named entity recognition (NER) to identify potential missed sales opportunities.
3. The application on sentiment analysis to improve customer experience using Natural Language Processing (NLP) techniques, VADER sentiment analysis, reduced the number of escalations by 90% for a quarter and was also recognized at a national level.
4. Developed a collaborative filtering based recommendation system during my internship and enhanced it using the Alternating Least Squares (ALS) algorithm. I also solved the cold start problem.
5. Build the machine learning team by conducting interviews and help in the hiring process for junior developers (lead/trained them in domain specific and technical skillset after onboarding).
Expectations
I am looking for a job which
1. Allows me to learn more and grow in my technical skillset.
2. Allows me to communicate more with the clients and gather business requirements.
3. Allows me to challenge myself everyday and solving those challenges motivates me.
Employment Preferences
Relocation destinations:
- Ontario, Canada
Expected Base Salary
**,000 CAD
Academic Degree
Experience
Total Professional Experience
Startup Experience
Big-Tech Companies
Enterprise Experience
Skills
- Python
- Machine Learning
- Supervised Learning
- Unsupervised Learning
- Predictive Modeling
- Feature Engineering
- Time-series Forecasting
- Data Analysis
- Data Pre-processing
- Exploratory Data Analysis
- EDA
- Data Visualization
- Hyper-parameter Optimization
- Model Building
- Model Evaluation
- Deep Learning
- Neural Networks
- Natural Language Processing
- NLP
- Tokenization
- Stemming
- Lemmatization
- Named Entity Recognition
- NER
- Spacy
- Keras
- Tensorflow
- Pytorch
- Pyspark
- Numpy
- Pandas
- Scikit-learn
- Sklearn
- Scipy
- Matplotlib
- Seaborn
- Natural Language Toolkit
- NLTK
- Plotly
- Dash
- Generative
- Generative Adversarial Networks
- GAN
- Diffusion Models
- HuggingFace
- Flask
- Dask
- Amazon Web Services
- AWS
- EC2
- Sagemaker
- Lex
- Polly
- Rekognition
- MongoDb
- SQL
- MySql
- Oracle Sql
- Jira
- Asana
- Taskade
- Research
- Publication
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