Data Scientist

We are on a mission to unlock impossible AI for all.

Imagine a world in which breakthrough discoveries are commonplace. Here at RAIC Labs, we help organizations transcend data-access and data-quality issues, unleashing the full impact of AI in all fields. Simply put, our technology unlocks previously impossible AI that has the power to change the world.

That’s all well and good, but despite our high-tech profile, we recognize that none of this is possible without our people. Which is why we’re thrilled to be adding a Data Scientist to our Technology team. 

Position Overview

As a Data Scientist at RAIC Labs, you will be at the forefront of driving our data culture, working to optimize user experiences through sophisticated data-driven insights and advanced computer vision techniques. You'll collaborate with cross-functional teams to build innovative solutions that leverage machine learning to enhance user acquisition, retention, and monetization. This role provides an opportunity to research state-of-the-art algorithms and apply them to real-world challenges in visual computing.

Here’s what you’ll be working on:

• Collaborate with product, engineering, and business teams to provide actionable insights through advanced data visualization and analysis.
• Research and implement cutting-edge algorithms in computer vision and machine learning for use in our unique applications, focusing on areas like image segmentation, object detection, and pattern recognition.
• Design and apply advanced machine learning techniques, statistical models, and neural networks to improve user engagement, conversion rates, and personalized features.
• Collaborate with software engineers to deploy machine learning models into production, ensuring scalability and real-time performance in a large-scale user environment.
• Continuously monitor, evaluate, and refine models to ensure the highest possible performance and alignment with our business objectives.

Are you up for the challenge? Read on to see if this will be the right fit for you! 

A good Data Scientist RAIC Labs must have the following skills, knowledge, education and experience:

• Ph.D or Master's degree in a relevant field (e.g., Computer Science, Data Science, Electrical Engineering, Mathematics, or a related discipline).
• 3-5+ years of experience in data science or a related field, preferably with a focus on computer vision and deep learning.
• Extensive experience with large-scale datasets, data collection methods, and real-world data analysis.
• Expertise in selecting and applying appropriate statistical methods and machine learning models for complex data problems.
• Proficiency in Python (including libraries such as TensorFlow, PyTorch, scikit-learn)
• understanding of object-oriented programming principles
• Familiarity with modern computer vision libraries such as OpenCV, YOLO, or similar frameworks.
• Strong theoretical and practical knowledge in machine learning, deep learning (CNNs, RNNs,GANS, etc.), and computer vision algorithms.
• Experience with hyperparameter tuning, model optimization, and working with large datasets in computer vision.
• Knowledge of traditional image processing techniques like edge detection, contour analysis, and optical flow is also helpful.
• Strong R&D capabilities, including experience designing experiments, testing hypotheses, and benchmarking models.
• Ability to stay up-to-date with the latest academic research in computer vision and related fields, with a track record of reading research papers (e.g., from CVPR, ICCV, NeurIPS) and translating findings into practical applications.
• Strong communication skills to explain complex technical results to non-technical stakeholders.
• Strong knowledge of Git for version control and collaboration in a team setting (forking, branching, merging)
• Experience building, maintaining, and deploying docker containers to virtual machines

If you want to go above and beyond, bring these skills and characteristics to the table:

• Experience with cloud-based data processing platforms and tools (e.g., AWS, GCP, Azure)
• Knowledge of 3D vision, depth estimation, or point cloud processing.
• Expertise in working with video data for tasks like action recognition, motion tracking, or video segmentation.