> Markdown version of [/jobs/ext/3104752-hcm-data-scientist](https://www.wearedevelopers.com/jobs/ext/3104752-hcm-data-scientist). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # [HCM] Data Scientist - **Company:** Mti Technologies Inc. - **Location:** United States (Remote available) - **Experience:** Experienced - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Amazon Web Services, Computer Vision, Microsoft Azure, Continuous Integration, Data Visualization, Github, Monitoring of Systems, Python (Programming Language), Machine Learning, Natural Language Processing, NumPy, Tensorflow, SQL Databases, Google Cloud, Pytorch, Deep Learning, Gitlab, Pandas, Containerization, Scikit Learn, Kubernetes, Information Technology, Production Code, Machine Learning Operations, Software Version Control, Docker - **Published:** September 27, 2026 - **Apply:** https://arc.dev/remote-jobs/j/redirect/pmvyrodbwg ## About the Role * Education: Bachelor's degree in Mathematics, Statistics, Data Science, Computer Science, AI, Engineering, or a highly related field is required. A Master's degree in any of the above disciplines is preferred. * Experience: Possess at least 3 years of experience in one or more data science domains such as machine learning, deep learning, natural language processing (NLP), computer vision (CV), optimization, or statistics. Technical Proficiency * Excellence in problem-solving, with the ability to formulate business problems into technical requirements in a scientifically rigorous and effective manner. * Proven experience leading data science workstreams or projects, and mentoring junior data scientists. * Fluent in English with strong communication skills, especially when engaging with non-technical business stakeholders. * Expert in Python and SQL along with relevant data science libraries (Pandas, NumPy, Scikit-learn, TensorFlow, PyTorch, etc.) and the ability to write production-ready code. * Advanced skills in version control (e.g. GitHub, GitLab, etc.) and data visualization. * Experience with MLOps tools and practices (e.g., pretraining, model tuning, model packaging, CI/CD for ML, model monitoring, ML pipelines, etc.). * Hands-on experience with cloud platforms (e.g., GCP, AWS, Azure) and containerization (e.g., Docker, Kubernetes). * Capable of researching, reading publications, and translating research papers into functional code. * Strong proactive mindset and the ability to take full responsibility for project outcomes. * Ability to lead the communication of technical concepts and collaborate effectively with cross-functional teams. * Ability to engage with business stakeholders to gather domain knowledge and requirements, and to communicate timelines, results, and other relevant matters. * Familiarity with agile delivery practices is a plus. ## Description As a Data Scientist, you will play a key role in bridging data science with business value by delivering high-impact solutions across diverse industries. You will work end-to-end-from understanding business requirements to developing models, evaluating outcomes, and deploying scalable solutions-while collaborating with cross-functional teams in a fast-paced, innovation-driven environment., *Your core responsibilities will be tailored to your level of seniority and finalized during the hiring process. * Solution Architecture & Design: Design robust, scalable, and innovative data science solutions while considering performance, maintainability, and business impact. * Technical Leadership: Provide technical leadership on complex projects, oversee the work of junior and mid-level data scientists, and ensure best practices are followed. * End-to-End Project Execution: Take ownership of data science projects and modules, from data collection and preparation to model development, evaluation, and MLOps. * Communication & Storytelling: Effectively communicate complex analytical concepts and findings to both technical and non-technical stakeholders, translating insights into actionable business recommendations. * Cross-functional Collaboration: Work closely with project managers, engineers, and business stakeholders to align data science efforts with business objectives. * Stakeholder Management: Engage with senior stakeholders across the organization to understand business needs, present complex findings, and influence data-driven decision-making at a strategic level. * Innovation & Research: Stay abreast of the latest advancements in data science, machine learning, and AI, exploring opportunities to integrate new technologies and methodologies. * Mentorship & Coaching: Actively mentor and coach Data Scientists at all levels, fostering a culture of continuous learning and growth within the team. ## Related Videos - [Vectorize all the things! 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