to stand out? Consulting Experience Databricks Machine Learning Associate or Machine Learning Professional Certification. Familiarity with traditional machine learning tools such as Python, SKLearn, XGBoost, SparkML, etc. Experience with deep learning frameworks like TensorFlow or PyTorch. Knowledge of ML model deployment options (e.g., Azure Functions, FastAPI, Kubernetes) for real-time More ❯
computer science fundamentals, including data structures, algorithms, data modelling, and software architecture Solid understanding of classical Machine Learning algorithms (e.g., Logistic Regression, Random Forest, XGBoost, etc.), state-of-the-art research areas (e.g., NLP, Transfer Learning, etc.), and modern Deep Learning algorithms (e.g., BERT, LSTM, etc.) Solid knowledge of SQL More ❯
computer science fundamentals, including data structures, algorithms, data modelling and software architecture Solid understanding of classical Machine Learning algorithms (e.g. Logistic Regression, Random Forest, XGBoost, etc), state-of-the-art research areas (e.g. NLP, Transfer Learning etc) and modern Deep Learning algorithms (e.g. BERT, LSTM, etc) Solid knowledge of SQL More ❯
with large datasets and with streaming data architectures. Demonstrated knowledge of large language models and generative AI. ML frameworks (Scikit-Learn, NumPy, SciPy, Pandas, XGBoost, Tensorflow, PyTorch, MXNet, LLM) Qualifications Required: Bachelor's Degree with 7 years experience; Master's Degree with 6 years experience; PhD with 2 years experience. More ❯
in a data science or analytics role. Strong proficiency in Python or R, and SQL. Experience with machine learning frameworks (e.g., scikit-learn, TensorFlow, XGBoost). Solid understanding of statistics, data modeling, and experimentation. Excellent communication skills and ability to translate complex findings into actionable insights. Excellent analytical and problem More ❯
problem-solving skills, business acumen, and the ability to translate complex models into actionable insights for non-technical stakeholders. Tools/Frameworks : Scikit-learn, XGBoost, LightGBM, StatsModels PyCaret, Prophet, or custom implementations for time series A/B testing frameworks (e.g., DoWhy, causalml) Programming & Data Tools : Python: Strong foundation in More ❯
problem-solving skills, business acumen, and the ability to translate complex models into actionable insights for non-technical stakeholders. Tools/Frameworks : Scikit-learn, XGBoost, LightGBM, StatsModels PyCaret, Prophet, or custom implementations for time series A/B testing frameworks (e.g., DoWhy, causalml) Programming & Data Tools : Python: Strong foundation in More ❯
unstructured data eg. Text, PDFs, jpgs, call recordings, video, etc. Knowledge of machine learning modelling techniques and how to fine-tune those models eg. XGBoost, Deep Neural Networks, Transformers, Markov chains, etc. Experience using specialized machine learning libraries e.g. Fastai, Keras, Tensorflow, pytorch, sci-kit learn, huggingface, etc. Must demonstrate More ❯
unstructured data eg. Text, PDFs, jpgs, call recordings, video, etc. Knowledge of machine learning modelling techniques and how to fine-tune those models eg. XGBoost, Deep Neural Networks, Transformers, Markov chains, etc. Experience using specialized machine learning libraries e.g. Fastai, Keras, Tensorflow, pytorch, sci-kit learn, huggingface, etc. Must demonstrate More ❯
unstructured data eg. Text, PDFs, jpgs, call recordings, video, etc. Knowledge of machine learning modelling techniques and how to fine-tune those models eg. XGBoost, Deep Neural Networks, Transformers, Markov chains, etc. Experience using specialized machine learning libraries e.g. Fastai, Keras, Tensorflow, pytorch, sci-kit learn, huggingface, etc. Must demonstrate More ❯
unstructured data eg. Text, PDFs, jpgs, call recordings, video, etc. Knowledge of machine learning modelling techniques and how to fine-tune those models eg. XGBoost, Deep Neural Networks, Transformers, Markov chains, etc. Experience using specialized machine learning libraries e.g. Fastai, Keras, Tensorflow, pytorch, sci-kit learn, huggingface, etc. Must demonstrate More ❯
trees, and other traditional machine learning models, translating conceptual ideas into actual solutions. Fluent in some of these machine learning frameworks such as SKLearn, XGBoost, PyTorch, or Tensorflow. Proficient in Python and able to transform abstract machine learning concepts into robust, efficient, and scalable solutions. Strong Computer Science fundamentals and More ❯
to the design, development, testing, and deployment of data science and AI solutions Experience and understanding of applied machine learning techniques in Python (e.g., xgboost, regression, decision trees) Experience with physics modelling highly desirable Practical knowledge and experience of developing AI solutions using advanced machine learning techniques (e.g., reinforcement learning More ❯
Python and SQL through Git, and applying other best practices to technical projects Experience and understanding of applied machine learning techniques in Python (e.g. xgboost, regression, decision trees) Practical knowledge and experience of developing AI solutions using advanced machine learning techniques (e.g. reinforcement learning, deep learning, LLMs) Experience of using More ❯
and technologies that are approved for use in government environments. Machine Learning. We apply best-of-breed machine learning frameworks (TensorFlow, scikit-learn, and xgboost) to create and deploy machine learning models for classification and regression. In addition to standard supervised machine learning techniques, we bring unique instance-based learning More ❯
NBA and MLB (ideally) Advanced knowledge of statistical modelling and machine learning, with relevant experience in the use of Python libraries (e.g., scikit-learn, xgboost, tensorflow, pymc3, statsmodels) and R equivalents Experience generating reports, dashboards, and data visualisations SQL experience as well as experience working with relational databases Excellent presentation More ❯
track record delivering ML/AI solutions in complex, real-world environments Strong Python skills and experience with key ML libraries (e.g., scikit-learn, XGBoost, PyTorch) Exposure to Generative AI technologies (e.g., LLMs, embeddings, RAG systems) Excellent communication skills and ability to engage senior stakeholders Nice to Have: Experience in More ❯
track record delivering ML/AI solutions in complex, real-world environments Strong Python skills and experience with key ML libraries (e.g., scikit-learn, XGBoost, PyTorch) Exposure to Generative AI technologies (e.g., LLMs, embeddings, RAG systems) Excellent communication skills and ability to engage senior stakeholders Nice to Have: Experience in More ❯
and implement machine learning and deep learning models to solve real-world business problems. Work with Python and key libraries such as PyTorch, TensorFlow, XGBoost, Scikit-Learn, LangChain, and Hugging Face. Apply algorithms including LLMs, transformers, classification, regression, clustering, and segmentation. Manage end to end ML pipelines: use case definition More ❯
basics but by year 3 should be quite proficient with at least pulling data Data Lake implementation and processing. Knowledge of Snowflake or equivalent XGBoost, LightGBM and the ability to use them for tabular data NLP and familiar with modern transformers Experience using Tableau or PowerBI for visualisations and reporting More ❯
new tools and packages where appropriate. Desired Skills & Experience: Core Technical Skills Expert in Python, SQL, and modern data science toolkits (e.g. scikit-learn, XGBoost, statsmodels). Solid grasp of dbt for data transformation. Experience with modern cloud data stacks - Snowflake, BigQuery, Redshift, etc. Comfortable working in agile environments with More ❯
s degree and 2+ years of applied research experience - Experience programming in Python and SQL - Experience with deep learning, machine learning with tree ensembles, XGBoost, LLMs, and relevant methods PREFERRED QUALIFICATIONS - PhD in engineering, computer science, machine learning, robotics, statistics, mathematics or equivalent quantitative field - Hands-on experience in building More ❯
categorical, image, to speech data. We operate at large scale and rely on state-of-the-art modeling techniques to power our ML models: XGBoost, BERT, Vision Transformers, Large Language Models. Minimum Qualifications 2+ years of building models for business application experience. PhD, or Master's degree and 4+ years More ❯
into business workflows. Desirable: Background in quantitative disciplines (math, stats, physics). Experience in finance, insurance, or ecommerce. Familiarity with ML frameworks like TensorFlow, XGBoost, and SKLearn. If this sounds like something you are interested in, please get in contact: thomas.deakin@spgresourcing.com SPG Resourcing is an equal opportunities employer and More ❯
into business workflows. Desirable: Background in quantitative disciplines (math, stats, physics). Experience in finance, insurance, or ecommerce. Familiarity with ML frameworks like TensorFlow, XGBoost, and SKLearn. If this sounds like something you are interested in, please get in contact: thomas.deakin@spgresourcing.com SPG Resourcing is an equal opportunities employer and More ❯
