messy datasets using Python. Practical expertise and work experience with ML projects, both supervised and unsupervised. Proficient programming skills with Python, including libraries such as NumPy, pandas, and scikit-learn, as well as R. Understanding and usage of the OpenAI API. NLP: tokenization, embeddings, sentiment analysis, basic transformers for text-heavy datasets. Experience with LLM & Prompt Engineering, including More ❯
required). 3+ years of professional experience in machine learning engineering. 2+ years of leadership or technical mentoring experience. Strong expertise in Python for machine learning (Pandas, NumPy, scikit-learn, etc.). Experience with deep learning frameworks such as TensorFlow, PyTorch, or JAX. Strong understanding of machine learning algorithms (supervised, unsupervised, reinforcement learning). Experience building and maintaining More ❯
required). 3+ years of professional experience in machine learning engineering. 2+ years of leadership or technical mentoring experience. Strong expertise in Python for machine learning (Pandas, NumPy, scikit-learn, etc.). Experience with deep learning frameworks such as TensorFlow, PyTorch, or JAX. Strong understanding of machine learning algorithms (supervised, unsupervised, reinforcement learning). Experience building and maintaining More ❯
required). 3+ years of professional experience in machine learning engineering. 2+ years of leadership or technical mentoring experience. Strong expertise in Python for machine learning (Pandas, NumPy, scikit-learn, etc.). Experience with deep learning frameworks such as TensorFlow, PyTorch, or JAX. Strong understanding of machine learning algorithms (supervised, unsupervised, reinforcement learning). Experience building and maintaining More ❯
such as RAG, LangChain, TensorFlow, and PyTorch. Exposure to LLMs from model families such as Anthropic, Meta, Amazon, and OpenAI. Familiarity with tools and packages like Pandas, NumPy, scikit-learn, Plotly/Matplotlib, and Jupyter Notebooks. Knowledge of ML-adjacent technologies, including AWS SageMaker and Apache Airflow. Proficiency in data pre-processing, data wrangling, and augmentation techniques. Experience More ❯
Job Responsibilities Design, develop, and deploy machine learning models to solve complex business problems. Collaborate with cross-functional teams to integrate ML models into production systems. Utilize PyTorch, Scikit-learn, NumPy, and Pandas for data analysis and model development. Develop and maintain APIs for model deployment and integration. Fine-tune large language models to enhance performance and accuracy. … Capabilities, And Skills Formal training or certification on Data engineering concepts and applied experience cProven experience in building and deploying machine learning models. Hands-on experience with PyTorch, Scikit-learn, NumPy, and Pandas. Proficient in Python programming language and building APIs. Solid understanding of statistics and machine learning theory. Experience with deep learning architectures, including LSTMs and Transformers. More ❯
and visualization tools to support AI model insights. · Write clean, well-documented, and secure code using Python, C C#, or R. · Leverage libraries such as TensorFlow, PyTorch, and Scikit-learn to prototype and optimize AI models. · Communicate complex technical concepts clearly to both technical and non-technical stakeholders. · Stay up-to-date with emerging AI trends, threat intelligence More ❯
Learning techniques. Practical exposure to GenAI projects and related frameworks (RAG apps, vector DBs, LangChain, LlamaIndex, agentic frameworks, ...) Advanced knowledge of Python and machine learning frameworks (SciPy, Scikit-learn, TensorFlow, PyTorch, pyMC, pgmpy, ...) Hands-on experience with one or more cloud computing platforms (Azure - preferred, AWS, GCP). Understanding of the whole ML lifecycle and experience More ❯
wider strategy by supporting innovation pilots, data quality initiatives, and automation projects. What You’ll Need Strong experience in Python, SQL, and common data science libraries (e.g. pandas, scikit-learn, NumPy). Experience in developing, testing, and deploying data models in a real-world business setting. Comfortable working with unstructured data and navigating ambiguity in fast-paced environments. More ❯
Job Role: Data & AI Science Consultant Location: Edinburgh Career Level: Consultant Accenture is a leading global professional services company, providing a broad range of services in strategy and consulting, interactive, technology and operations, with digital capabilities across all of these More ❯
messy datasets using Python. Practical expertise and work experience with ML projects, both supervised and unsupervised. Proficient programming skills with Python, including libraries such as NumPy, pandas, and scikit-learn, as well as R. Understanding and usage of the OpenAI API. NLP: tokenization, embeddings, sentiment analysis, basic transformers for text-heavy datasets. Experience with LLM & Prompt Engineering, including More ❯
Job Description About this job SRG are seeking a highly motivated and skilled data scientist to join our client and to focus on leveraging their proprietary platform to develop novel gene control systems for the cell and gene therapy space. More ❯
learning. Optimization : Continuously improve machine learning infrastructure and production workflows. Strong technical foundation in machine learning and software engineering Proficiency in Python and ML libraries (e.g., TensorFlow, PyTorch, scikit-learn) Experience with cloud platforms (AWS, GCP, Azure) Experience with CI/CD pipelines for machine learning (e.g., Vertex AI) Familiarity with data processing tools like Apache Beam/ More ❯
Glasgow, Scotland, United Kingdom Hybrid / WFH Options
WARACLE
not essential Demonstrable interest in Artificial Intelligence, Machine Learning, Deep Learning, or Natural Language Processing. Some hands-on experience with AI concepts, libraries, or frameworks (e.g., TensorFlow, PyTorch, scikit-learn, Hugging Face Transformers, Keras, etc.). Familiarity with Python for AI/ML development is a significant advantage. Experience with data engineering pipelines or big data technologies (e.g. More ❯
Glasgow, Scotland, United Kingdom Hybrid / WFH Options
Waracle
but not essential Demonstrable interest in Artificial Intelligence, Machine Learning, Deep Learning, or Natural Language Processing.Some hands-on experience with AI concepts, libraries, or frameworks (e.g., TensorFlow, PyTorch, scikit-learn, Hugging Face Transformers, Keras, etc.) Familiarity with Python for AI/ML development is a significant advantage. Experience with data engineering pipelines or big data technologies (e.g., Kafka More ❯
business Effective verbal and written communication skills Experience working with real-world data sets and building scalable models from big data Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow Experience with large scale distributed systems Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability More ❯
experience. Experience programming in Java, C++, Python, or related language. Experience with neural deep learning methods and machine learning. PREFERRED QUALIFICATIONS Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy, etc. Experience with large scale distributed systems such as Hadoop, Spark, etc. Our inclusive culture empowers Amazonians to deliver the best results More ❯
experience in system design, application development, testing, and operational stability. Proficient in coding in Python. Proficient in the use of basic data science libraries in Python (NumPy, pandas, scikit-learn, pyspark). Experience in developing, debugging, and maintaining code in a large corporate environment with modern programming and database querying. Overall knowledge of the Software Development Life Cycle. More ❯
Python or related language - Experience with neural deep learning methods and machine learning - Experience with prompting techniques for LLMs PREFERRED QUALIFICATIONS - Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc. - Experience with large scale distributed systems such as Hadoop, Spark etc. - PhD in math/statistics/engineering or other equivalent More ❯
experience in system design, application development, testing, and operational stability. Proficient in coding in Python. Proficient in the use of basic data science libraries in Python (NumPy, pandas, scikit-learn, pyspark). Experience in developing, debugging, and maintaining code in a large corporate environment with modern programming languages and database querying languages. Overall knowledge of the Software Development More ❯
methods, regression, properties of distributions, weighting sample-based data, and proper use of statistical tests in real-world applications. Proficiency with Python libraries such as NumPy, SciPy, Pandas, scikit-learn, and others related to data and machine learning. Working knowledge of SQL, data structures, and databases (Snowflake is desirable). This organization is pragmatic and humble, seeking like More ❯
statistical and analytical techniques and concepts sampling methods, Regression Properties of distributions Weighting sample-based data Statistical tests proper usage Real-world applications. Python - NumPy, SciPy, Pandas, MLlib, scikit-learn, and other common data and machine learning related libraries Working knowledge of SQL, data structures and databases (Snowflake - desirable) This is a pragmatic and humble organisation who are More ❯
statistical and analytical techniques and concepts sampling methods, Regression Properties of distributions Weighting sample-based data Statistical tests proper usage Real-world applications. Python - NumPy, SciPy, Pandas, MLlib, scikit-learn, and other common data and machine learning related libraries Working knowledge of SQL, data structures and databases (Snowflake - desirable) This is a pragmatic and humble organisation who are More ❯
statistical and analytical techniques and concepts sampling methods, Regression Properties of distributions Weighting sample-based data Statistical tests proper usage Real-world applications. Python - NumPy, SciPy, Pandas, MLlib, scikit-learn, and other common data and machine learning related libraries Working knowledge of SQL, data structures and databases (Snowflake - desirable) This is a pragmatic and humble organisation who are More ❯
statistical and analytical techniques and concepts sampling methods, Regression Properties of distributions Weighting sample-based data Statistical tests proper usage Real-world applications. Python - NumPy, SciPy, Pandas, MLlib, scikit-learn, and other common data and machine learning related libraries Working knowledge of SQL, data structures and databases (Snowflake - desirable) This is a pragmatic and humble organisation who are More ❯
