AI ML Data Science Engineer

United Software Group Inc. - Canada; Canada; Remote; CAD 80,000 - 120,000; Full time

Job summary

Join a forward-thinking company as an AI ML Data Science Engineer, where you'll leverage your expertise in NLP, model fine-tuning, and advanced data science techniques to develop cutting-edge AI solutions for healthcare. This remote role offers the chance to work on innovative projects, utilizing your skills in multimodal prompting, speech recognition, and sentiment analysis to create impactful AI models. If you're passionate about transforming healthcare through AI and want to be part of a dynamic team, this opportunity is perfect for you.

Qualifications

• Expertise in AI skills for healthcare, including NLP and model fine-tuning.
• Strong foundation in data science and machine learning techniques.

Responsibilities

• Develop and refine AI models for healthcare applications.
• Utilize advanced data science techniques for model training and evaluation.

Skills

NLP for healthcare

Prompt Engineering

Multimodal Prompting

Evaluation and Refinement

Model Fine-Tuning

Speech Recognition

Text-to-Speech

Audio Signal Processing

Speech to Text Expertise

Sentiment & Tone Analysis Expertise

LLM Expertise

Computer Vision

Embeddings Models

Recommendation Algorithms

Neural Collaborative Filtering

Neural Network

Basic Knowledge in Azure Databricks

Knowledge in Healthcare Domain

Data Annotation & Labeling

Model Training

Fine Tuning

Supervised & Unsupervised Learning

Risk Prediction

Model Evaluation, Selection & Fine-tuning

Dimensionality Reduction

Vector Search Optimization

Feature Engineering

Data Drift Monitoring & Identification

Synthetic Data Generation

Tools

TensorFlow

Azure Databricks

Job description

Job Posting Title: AI ML Data Science Engineer

Location: Halifax, CA (Remote)

Must have AI Skills:

1. NLP for healthcare: Specialized natural language processing techniques tailored for medical data.
2. Prompt Engineering: Crafting effective prompts for AI models, especially important for large language models (LLMs).
3. Multimodal Prompting: Designing prompts that work across different AI tools and models.
4. Evaluation and Refinement: Assessing AI outputs and refining prompts for better results.
5. Model Fine-Tuning: Adjusting pre-trained models to improve performance on specific tasks.
6. Speech Recognition: Converting spoken language into text.
7. Text-to-Speech: Generating spoken language from text.
8. Audio Signal Processing: Analyzing and manipulating audio signals.
9. Speech to Text Expertise: Advanced skills in converting speech to text accurately.
10. Sentiment & Tone Analysis Expertise: Analyzing emotions and tone in text data.
11. LLM Expertise: Working with large language models like GPT-4.
12. Computer Vision (image processing & OCR): Analyzing and interpreting visual data, including optical character recognition.
13. Embeddings Models (TensorFlow/Phoenix): Using embeddings for various ML tasks.
14. Expertise in Knowledge retrieval systems & LLM Integration with retrieval.
15. Recommendation Algorithms: Building systems to suggest items to users.
16. Neural Collaborative Filtering: Using neural networks for recommendation systems.
17. Neural Network: Designing & implementing models.
18. Basic Knowledge in Azure Databricks infrastructure.
19. Knowledge in Healthcare Domain.

Data Science and Machine Learning Skills:

1. Data Annotation & Labeling: Essential for creating high-quality training datasets.
2. Model Training: Building and training machine learning models.
3. Fine Tuning: Adjusting pre-trained models to improve performance on specific tasks.
4. Supervised & Unsupervised Learning: Techniques for both labeled and unlabeled data.
5. Risk Prediction (time series models - LSTMs, ARIMA) & Survival Analysis Techniques: Predicting future events and analyzing time-to-event data.
6. Model Evaluation, Selection & Fine-tuning: Assessing and optimizing model performance.
7. Dimensionality Reduction: Reducing the number of features in a dataset.
8. Vector Search Optimization: Enhancing search algorithms using vector representations.
9. Feature Engineering: Creating new features from raw data to improve model performance.
10. Data Drift Monitoring & Identification: Detecting changes in data distributions over time.
11. Synthetic Data Generation: Creating artificial data for training models when real data is scarce.
12. Basic Knowledge in Azure Databricks infrastructure.
13. Knowledge in Healthcare Domain.