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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # [HCM] Data Scientist (Middle, Senior level) - Hands-on Modeling - **Company:** Mti Technologies Inc. - **Location:** United States (Remote available) - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** A/B Testing, Artificial Intelligence, Airflow, Amazon Web Services, Computer Vision, Microsoft Azure, Cloud Computing, Software Quality, Continuous Integration, Data Cleansing, Information Engineering, Extract Transform Load (ETL), Data Warehousing, Monitoring of Systems, Python (Programming Language), Machine Learning, Natural Language Processing, NumPy, Tensorflow, SQL Databases, Feature Engineering, Chatbots, Pytorch, Retrieval-Augmented Generation, Large Language Models, Prompt Engineering, Deep Learning, Kaggle, Pandas, Containerization, Scikit Learn, Kubernetes, Information Technology, Statistics Packages, Xgboost, Machine Learning Operations, Docker - **Published:** November 12, 2026 - **Apply:** https://arc.dev/remote-jobs/j/redirect/pyvu447yy3 ## About the Role Must have: * Education: Bachelor's degree in Mathematics, Statistics, Data Science, Computer Science, AI, Engineering, or a highly related field. A Master's degree is preferred. * Experience: At least 3 years of hands-on experience in a Data Scientist role in industry, building ML or statistical models (for example machine learning, deep learning, NLP, computer vision, optimization, forecasting or statistics). * Strong Python and SQL, with solid experience in DS libraries (Pandas, NumPy, Scikit-learn, XGBoost / LightGBM, PyTorch or TensorFlow, statsmodels, etc.). * Solid foundation in statistics and machine learning: you understand the methods you use, not only how to call them. * Proven delivery of at least one model used in a real business process or product, with a result you can describe. * Good English communication, especially with non-technical business stakeholders. Nice to have: * MLOps experience (ML pipelines, model packaging, CI/CD for ML, model monitoring, MLflow / Airflow / Vertex AI / SageMaker). * Cloud (GCP, AWS or Azure) and containerization (Docker, Kubernetes). * Experimentation design, A/B testing or causal inference. * Experience working with international clients or in a consulting / outsourcing environment. * Familiarity with agile delivery practices. ## Description We are looking for a hands-on Data Scientist who builds models with their own hands and can explain every choice behind them. You will take business problems from our clients across diverse industries, turn them into data science problems, and deliver end-to-end: data preparation, feature engineering, modeling, evaluation, and putting the model into use. This is an individual-contributor role first: you write the code, run the experiments, and own the results., * Your main job for the last 3+ years has been Data Scientist in industry (not internship, not academic research). * You have built predictive or statistical models yourself (for example churn, recommendation, forecasting, pricing, risk, segmentation, NLP or CV models) and taken them beyond the notebook. * You can go deep on your own projects: why you chose that algorithm, how you validated it, what went wrong, and what the business impact was. * You still write Python and SQL every day and can review other people's code, not only their results. This role is probably not the right fit if: * Your experience is mainly AI Engineering / LLM application building (RAG, agents, chatbots, prompt engineering) without classical modeling work. * Your experience is mainly Data Engineering (pipelines, ETL, data warehouse) or Data Analyst / BI (dashboards, reporting, SQL analysis). * Your data science experience comes mainly from academic research, lecturing, courses, bootcamps or Kaggle, with less than 3 years in an industry DS role. * You have moved into a manager or product lead role and no longer code hands-on., *Your core responsibilities will be tailored to your level of seniority and finalized during the hiring process. * Problem framing: Work with clients and business stakeholders to understand their needs and translate them into clear, measurable data science problems. * Hands-on modeling: Explore data, engineer features, and build, tune and validate machine learning and statistical models (supervised, unsupervised, time series, experimentation / A/B testing). * Scientific rigor: Choose the right method for the problem, design sound evaluation, and be able to defend your assumptions and results. * End-to-end delivery: Take models from experiment to production together with engineers, including packaging, deployment, monitoring and iteration (MLOps). * Communication & storytelling: Present findings and recommendations clearly in English to both technical and non-technical stakeholders. * Code quality: Write clean, production-ready, version-controlled code and review the code of teammates. * Continuous learning: Follow new methods in data science and ML, and turn papers or new techniques into working code when they add value. * (Senior level) Guide the technical direction of DS workstreams and support junior teammates through code and design reviews.