12 of 12 Permanent Feature Engineering Jobs in the South East

experienced Data & ML Contractor to deliver a proof of concept using static, structured datasets. This is a focused, pragmatic engagement centred on data preparation, feature engineering, and anomaly detection - with an emphasis on clear, interpretable outputs for stakeholders. This is not a heavy Data Engineering or MLOps … analytics (e.g., pandas, numpy; scikit-learn or equivalent) Structured data expertise: joins, aggregations, data cleaning, handling missing data/outliers, basic data modelling concepts Feature engineering: ability to craft interpretable, business-relevant features Anomaly detection experience: practical knowledge of rule-based and statistical methods; ML-based approaches where ...

experienced Data & ML Contractor to deliver a proof of concept using static, structured datasets. This is a focused, pragmatic engagement centred on data preparation, feature engineering, and anomaly detection - with an emphasis on clear, interpretable outputs for stakeholders. This is not a heavy Data Engineering or MLOps … analytics (e.g., pandas, numpy; scikit-learn or equivalent) Structured data expertise: joins, aggregations, data cleaning, handling missing data/outliers, basic data modelling concepts Feature engineering: ability to craft interpretable, business-relevant features Anomaly detection experience: practical knowledge of rule-based and statistical methods; ML-based approaches where ...

experienced Data & ML Contractor to deliver a proof of concept using static, structured datasets. This is a focused, pragmatic engagement centred on data preparation, feature engineering, and anomaly detection - with an emphasis on clear, interpretable outputs for stakeholders. This is not a heavy Data Engineering or MLOps … analytics (e.g., pandas, numpy; scikit-learn or equivalent) Structured data expertise: joins, aggregations, data cleaning, handling missing data/outliers, basic data modelling concepts Feature engineering: ability to craft interpretable, business-relevant features Anomaly detection experience: practical knowledge of rule-based and statistical methods; ML-based approaches where ...

experienced Data & ML Contractor to deliver a proof of concept using static, structured datasets. This is a focused, pragmatic engagement centred on data preparation, feature engineering, and anomaly detection - with an emphasis on clear, interpretable outputs for stakeholders. This is not a heavy Data Engineering or MLOps … analytics (e.g., pandas, numpy; scikit-learn or equivalent) Structured data expertise: joins, aggregations, data cleaning, handling missing data/outliers, basic data modelling concepts Feature engineering: ability to craft interpretable, business-relevant features Anomaly detection experience: practical knowledge of rule-based and statistical methods; ML-based approaches where ...

experienced Data & ML Contractor to deliver a proof of concept using static, structured datasets. This is a focused, pragmatic engagement centred on data preparation, feature engineering, and anomaly detection - with an emphasis on clear, interpretable outputs for stakeholders. This is not a heavy Data Engineering or MLOps … analytics (e.g., pandas, numpy; scikit-learn or equivalent) Structured data expertise: joins, aggregations, data cleaning, handling missing data/outliers, basic data modelling concepts Feature engineering: ability to craft interpretable, business-relevant features Anomaly detection experience: practical knowledge of rule-based and statistical methods; ML-based approaches where ...

experienced Data & ML Contractor to deliver a proof of concept using static, structured datasets. This is a focused, pragmatic engagement centred on data preparation, feature engineering, and anomaly detection - with an emphasis on clear, interpretable outputs for stakeholders. This is not a heavy Data Engineering or MLOps … analytics (e.g., pandas, numpy; scikit-learn or equivalent) Structured data expertise: joins, aggregations, data cleaning, handling missing data/outliers, basic data modelling concepts Feature engineering: ability to craft interpretable, business-relevant features Anomaly detection experience: practical knowledge of rule-based and statistical methods; ML-based approaches where ...

experienced Data & ML Contractor to deliver a proof of concept using static, structured datasets. This is a focused, pragmatic engagement centred on data preparation, feature engineering, and anomaly detection - with an emphasis on clear, interpretable outputs for stakeholders. This is not a heavy Data Engineering or MLOps … analytics (e.g., pandas, numpy; scikit-learn or equivalent) Structured data expertise: joins, aggregations, data cleaning, handling missing data/outliers, basic data modelling concepts Feature engineering: ability to craft interpretable, business-relevant features Anomaly detection experience: practical knowledge of rule-based and statistical methods; ML-based approaches where ...

building the future of population health management. As a Senior Machine Learning Engineer, you will sit at the intersection of data science, platform engineering, and production systems. Your mission is to design and operate the foundations that allow ML, analytics, and agentic AI systems to move safely and reliably … from experimentation into regulated healthcare production environments on Google Cloud Platform. You will be the primary interface between Data Science and Platform Engineering, enabling rapid iteration while enforcing production and compliance standards. Responsibilities Design and implement Onsera's agentic AI platform – architect LLM/agent framework selection, standardized patterns ...

Drive experimentation frameworks, hypothesis testing, model validation, and statistical rigor Ensure robustness, generalisation, bias mitigation, and explainability in deployed models Provide technical direction on feature engineering strategies and model performance enhancement Collaborate with engineering teams to transition models into scalable production systems Mentor data scientists and uphold … 8+ years of hands-on experience in machine learning and data science Advanced degree (Master's or PhD preferred) in Computer Science, Statistics, Mathematics, Engineering, or related quantitative discipline Proven experience building and deploying advanced ML and deep learning models in enterprise environments Deep understanding of algorithm selection, model ...

clients. As a senior leader, you will interact frequently with customers, provide expert opinions, and contribute to HCL's strategic vision. Key Responsibilities Technical & Engineering Leadership • Design and implement AI and cloud architectures, integrating GenAI technologies to enhance functionality and scalability. • Lead architectural discussions with clients, providing expert guidance … Skills & Experience • Deep knowledge of machine learning operations (MLOps) and experience in deploying, monitoring, and maintaining AI models in production environments. • Proficiency in data engineering for AI, including data preprocessing, feature engineering, and pipeline creation. • Expertise in AI model fine-tuning and evaluation, with a focus ...

cloud and on-prem data platforms , automation, and emerging AI-driven capabilities, and is specifically suited to candidates with strong, hands-on data engineering experience , particularly in building, operating, and optimising complex data pipelines, data models, and integration workflows at scale. Role Overview and Responsibilities As a Data & Automation … delivery processes using technologies such as Azure Data Factory, Databricks, SSIS, and SQL . Reducing manual interventions through automation and standardisation , including data preparation, feature engineering, and training-data pipelines. Preparing data models and datasets (DWH/Lakehouse) to support business intelligence, analytics, and operational reporting. Monitoring ...

Forum, Metro Ethernet Forum, IETF, GSMA, Open Broadband etc. Working knowledge of classical AI and machine learning techniques, including supervised and unsupervised learning, feature engineering, model training, evaluation and deployment. Working knowledge of next‐generation AI approaches, including generative AI and agentic systems, such as large language models ...