51 to 75 of 261 scikit-learn Jobs

you We don't expect perfection, but for this senior role we are looking for someone with: Strong Python skills, particularly within the data science ecosystem (Pandas, NumPy, scikit-learn, PyTorch/TensorFlow, visualisation libraries) 4+ years of experience delivering machine learning products end-to-end in production Hands-on experience with LLMs - fine-tuning, prompt engineering, vector More ❯

product and data teams Optimise model performance for accuracy, speed, and cost Run experiments, A/B tests, and validate approaches statistically Use tools like Python, TensorFlow, PyTorch, Scikit-learn, MLflow, SQL, Docker, AWS/GCP What Youll Need: Bachelors degree in Computer Science, AI, Machine Learning, or related field 1+ year of experience in a Machine Learning More ❯

product and data teams Optimise model performance for accuracy, speed, and cost Run experiments, A/B tests, and validate approaches statistically Use tools like Python, TensorFlow, PyTorch, Scikit-learn, MLflow, SQL, Docker, AWS/GCP What Youll Need: Bachelors degree in Computer Science, AI, Machine Learning, or related field 1+ year of experience in a Machine Learning More ❯

qualifications: Hold an active Security clearance (Ideally Top Secret with SCI Poly) Strong programming skills in Python, with experience in machine learning libraries such as TensorFlow, PyTorch, or scikit-learn Proficient in data manipulation, feature engineering, and model evaluation techniques Solid understanding of different machine learning algorithms and their applications Experience deploying machine learning models into production environments More ❯

on experience in AI or ML in a professional environment (3-5 years preferred) Strong knowledge of machine learning model development, deployment, and modern ML libraries (TensorFlow, PyTorch, scikit-learn, etc.) Solid programming background with experience using statistical and signal analysis libraries Experience with neural network architectures including deep learning models Understanding of transformer architectures and attention mechanisms More ❯

machine learning Proven experience working with consumer behaviour data—ideally within healthcare, digital health, or related B2C environments Strong programming skills in Python, with experience using libraries like scikit-learn, XGBoost, and pandas Practical experience in MLOps or strong knowledge of model deployment (e.g. MLflow, Airflow, Docker, Kubernetes, model monitoring tools) Familiarity with cloud environments (AWS, GCP, or More ❯

machine learning Proven experience working with consumer behaviour data—ideally within healthcare, digital health, or related B2C environments Strong programming skills in Python, with experience using libraries like scikit-learn, XGBoost, and pandas Practical experience in MLOps or strong knowledge of model deployment (e.g. MLflow, Airflow, Docker, Kubernetes, model monitoring tools) Familiarity with cloud environments (AWS, GCP, or More ❯

Strong python programming skills as evidenced by earlier work in data science or software engineering. An excellent command of the basic libraries for data science (e.g. NumPy, Pandas, Scikit-Learn) and familiarity with a deep-learning framework (e.g. TensorFlow, PyTorch, Caffe) A high level of mathematical competence and proficiency in statistics A solid grasp of essentially all of More ❯

and deploying LLMs, with tools such as HuggingFace, Langchain, and OpenAI Extensive hands-on industry data science experience, leveraging typical machine learning and data science tools including pandas, scikit-learn, and TensorFlow/PyTorch Experience building production-grade machine learning deployments on AWS, Azure, or GCP Experience communicating and/or teaching technical concepts to non-technical and More ❯

and deploying LLMs, with tools such as HuggingFace, Langchain, and OpenAI Extensive hands-on industry data science experience, leveraging typical machine learning and data science tools including pandas, scikit-learn, and TensorFlow/PyTorch Experience building production-grade machine learning deployments on AWS, Azure, or GCP Experience communicating and/or teaching technical concepts to non-technical and More ❯

we do require the ability to become a fluent Python programmer in a short timeframe An excellent command of the basic libraries for data science (e.g. NumPy, Pandas, Scikit-Learn) and familiarity with a deep-learning framework (e.g. TensorFlow, PyTorch, Caffe) A high level of mathematical competence and proficiency in statistics A solid grasp of essentially all of More ❯

we do require the ability to become a fluent Python programmer in a short timeframe An excellent command of the basic libraries for data science (e.g. NumPy, Pandas, Scikit-Learn) and familiarity with a deep-learning framework (e.g. TensorFlow, PyTorch, Caffe) A high level of mathematical competence and proficiency in statistics A solid grasp of essentially all of More ❯

implementing efficient data pipelines, including performance tuning and optimization. Proven ability to apply machine learning techniques to real-world problems. Familiarity with core libraries such as Pandas, NumPy, Scikit-learn, SciPy, and Polars is expected. Experience with digital signal processing, mobile sensor physics, or behavioural signal design. Proven track record of designing and delivering scalable data products, driving More ❯

/experience: Proven track record of delivering commercial value using supervised machine learning techniques. Advanced Python programmer, and experience with data manipulation and analysis libraries (e.g., NumPy, Pandas, scikit-learn) Advanced knowledge of data science technologies including tooling and platforms. Proficient in SQL and Git. Ability to drive projects from initiation to completion. Keen interest in emerging Machine More ❯

code. Strong understanding of data structures, data modeling, and software architecture Data Science Library Knowledge: Deep understanding of key Data Science and Machine Learning libraries (e.g., pandas, NumPy, scikit-learn, TensorFlow), with a preference for PySpark experience Model Productionisation: Experience in taking Machine Learning models from development to production CI/CD and MLOps Experience : Familiarity with Continuous More ❯

frameworks. High-growth Experience: Prior experience working in high-growth environments, ideally start-ups or scale-ups Coding Skills: Proficient in Python, SQL, and one of Pytorch, Tensorflow, Scikit-learn, with daily experience in writing, debugging, and optimising code. ML Ops Knowledge: Familiarity with tools like MLflow, Kubeflow, or Vertex AI, and experience implementing CI/CD pipelines More ❯

C++, or Java, and their associated security considerations. * Previous experience with ML, LLM, deep learning and data manipulation techniques, libraries, and frameworks such as TensorFlow, PyTorch, Jax, and scikit-learn is desirable. * Experience in implementing secure coding practices, DevOps, CI/CD pipelines and familiarity with secure software development life cycle (SDLC) methodologies. * Strong understanding of modern software More ❯

NumPy). Advanced experience with SQL for data querying and manipulation. Experience with Git, Bash, Docker, and machine learning pipelines. Experience with open-source machine learning libraries like scikit-learn, PyTorch, TensorFlow, and SciPy. Hands-on experience working with multi-modal data (images, text) and relevant ML techniques. Experience with cloud technologies and data storage solutions, including Snowflake. More ❯

NumPy). Advanced experience with SQL for data querying and manipulation. Experience with Git, Bash, Docker, and machine learning pipelines. Experience with open-source machine learning libraries like scikit-learn, PyTorch, TensorFlow, and SciPy. Hands-on experience working with multi-modal data (images, text) and relevant ML techniques. Experience with cloud technologies and data storage solutions, including Snowflake. More ❯

learning models to contribute to company growth efforts, impacting revenue and other key business outcomes Advanced understanding of Python and the machine learning ecosystem in Python (Numpy, Pandas, Scikit-learn, LightGBM, PyTorch) Knowledge of SQL and experience with relational databases Agile, action-oriente Nice to have Experience working in cloud platforms (AWS, GCP, Microsoft Azure) Relevant knowledge or More ❯

collaborative, face-to-face, and iterative development environment. Skills & Experience Strong foundation in applied mathematics, statistics, or machine learning. Experience building and deploying models in Python (pandas, NumPy, scikit-learn, etc.). Comfort working with large and complex datasets, including data wrangling and exploratory analysis. Experience (or strong interest) in GPU-powered model development (e.g. using PyTorch, TensorFlow More ❯

collaborative, face-to-face, and iterative development environment. Skills & Experience Strong foundation in applied mathematics, statistics, or machine learning. Experience building and deploying models in Python (pandas, NumPy, scikit-learn, etc.). Comfort working with large and complex datasets, including data wrangling and exploratory analysis. Experience (or strong interest) in GPU-powered model development (e.g. using PyTorch, TensorFlow More ❯

collaborative, pragmatic approach to working in cross-functional Agile squads. Your experience: Expert-level fluency in Python (required) with deep experience in ML, OR, and DS libraries (e.g. scikit-learn, pandas, numpy, Gurobi). Strong knowledge of machine learning and optimisation techniques-including supervised, unsupervised learning, and operations research methods. Solid background in software engineering for data science More ❯

data science or data engineering solutions into production. You're comfortable writing production-grade code, not just notebooks. Strong Python and SQL skills, including the basic libraries (Pandas, Numpy, ScikitLearn). You value writing clean, maintainable, and tested code. Proven ability to design, build, and maintain data pipelines and tools from scratch that are reliable, maintainable, and scalable. Strong communicator More ❯

biggest names in private equity. Ideal Candidate Experience: 5+ years in data science roles, preferably in fast-moving or early-stage environments Languages & Tools: Strong Python (Pandas, NumPy, Scikit-learn, TensorFlow or PyTorch) Advanced SQL AWS MLOps tooling (e.g., MLflow, SageMaker, or similar) Bonus Points For: Knowledge of LLMs, RAG pipelines, prompt engineering, or agentic interfaces Experience with More ❯