Mid Level Data Scientist

Job Overview:

We are looking for a passionate and driven Mid-Level Data Scientist to join our team. In this role, you will develop and implement machine learning models, analyze complex data sets, and contribute to data-driven decision-making. You will work closely with cross-functional teams to solve challenging business problems and drive innovation. This role requires strong analytical skills, a deep understanding of machine learning, and the ability to translate data insights into actionable strategies.

Responsibilities:

• Develop and implement machine learning models and algorithms to solve business challenges.
• Analyze large and complex data sets to extract meaningful insights and trends.
• Collaborate with cross-functional teams, including Product, Engineering, and Business, to define data-driven solutions.
• Optimize data pipelines and architectures to support model development and deployment.
• Stay updated with the latest trends in machine learning, AI, and data science to continuously improve our methodologies.
• Ensure data integrity, security, and compliance with company policies and industry regulations.
• Document and communicate findings, methodologies, and technical solutions to both technical and non-technical stakeholders.
• Contribute to the team’s knowledge-sharing culture and mentor junior team members when needed.

Requirements

• Bachelor’s or Master’s degree in Computer Science, Data Science, or a related field.
• 3+ years of hands-on experience in data science and machine learning.
• Experience in OCR, face matching, predictive analytics, and Large Language Models (LLMs) is a plus.
• Proficiency in Python and ML frameworks such as TensorFlow, PyTorch, and OpenCV.
• Solid understanding of machine learning, deep learning, and NLP techniques.
• Strong problem-solving skills with a keen analytical mindset.
• Ability to work independently as well as collaborate effectively with a team.
• Good communication skills to convey complex data insights in an understandable way.
• Experience with cloud platforms such as AWS, Google Cloud, or Azure.
• Exposure to deploying machine learning models in production environments.
• Knowledge of data privacy regulations and compliance.
• Contributions to the data science community (research, open-source projects, publications).
• Familiarity with agile development methodologies.
• Understanding of business and product strategy in data-driven decision-making.