21 of 21 Remote Feature Engineering Jobs

for offline use. • Collaborate with cross-functional teams to understand product requirements and translate them into data-driven solutions. • Implement data preprocessing, cleaning, and feature engineering to ensure high-quality input for machine learning models. • Evaluate model performance using appropriate metrics and continuously optimize for accuracy, efficiency, and … language processing and LLMs. • Proficiency in programming languages such as Python and libraries like TensorFlow, PyTorch, or similar. • Strong understanding of data preprocessing techniques, feature engineering, and model evaluation metrics. • Experience with secure data handling and the ability to work within restricted environments. • Excellent problem-solving skills and More ❯

maintainActive DoD Secret Clearance Knowledge Requirements and Qualifications: Bachelor's or Master's of Science in Data Science, Computer Science, AI/ML, Software Engineering, Computational Science, Information Systems (with Data Analytics or AI Concentration) or related degree Technical Skills: Programming & Data Tools Proficiency in Python and R for … the end-to-end ML lifecycle: data prep, model training, evaluation, deployment, and monitoring. Familiarity with version control systems (e.g., Git) and basic software engineering practices. Database and Data Handling Working knowledge of SQL and NoSQL databases (e.g., MongoDB, Apache Cassandra, Oracle, mySQL). Experience building or consuming ETL … Bonus Skills Advanced NLP & LLM Techniques Hands-on experience with large language models (LLMs) using OpenAI API, RAG, or embedding models. Familiarity with prompt engineering for model fine-tuning or inference optimization. Understanding of vector databases (e.g., Qdrant, Pinecone) and semantic search techniques. Use of MLOps tools for CI More ❯

premier analytics consulting firm with a mission to drive business value for clients through expertise in data strategy, data analytics, decision sciences, and data engineering and infrastructure. This position is within our decision sciences practice which focuses on delivering solutions via machine learning and statistical modelling. What you will … with clients to design end-to-end analytical solutions Work independently to solve complex data science use-cases across various industries Design and develop feature engineering pipelines, build ML & AI infrastructure, deploy models, and orchestrate advanced analytical insights Write code in SQL, Python, and Spark following software engineering … do so through thoughtfulness, prioritization, and seeing a solution through from brainstorming to deployment. In particular you have these traits: Degree in Computer Science, Engineering, Mathematics, or equivalent experience. Experience with building high quality Data Science models to solve a client's business problems Experience with managing stakeholders and More ❯

are a global leader in partnering with companies to transform and manage their business by harnessing the power of technology. The world leader in engineering services, we bring together a global team of engineers, scientists, and architects to help the world's most innovative companies unleash their potential. From … autonomous cars to life-saving robots, our digital and software technology experts think outside the box as they provide unique R&D and engineering services across all industries. Join us for a career full of opportunities. Where you can make a difference. Where no two days are the same. … work at home 100% of the time. Your role • Design, develop, and deploy AI/ML models and solutions, including LLMs and GenAI. • Perform feature engineering and selection to optimize model performance. • Select and implement appropriate AI/ML algorithms, including supervised, unsupervised, and reinforcement learning models. • Train More ❯

OpenAI GPT or Anthropic Claude. Design and implement RAG pipelines for scalable, real-time applications leveraging vector databases like Pinecone, Weaviate, Opensearch. Develop prompt engineering strategies to optimize model outputs for specific use cases. Design and deploy scalable ML models that integrate with existing systems. End-to-End ML … Pipeline: Architect, train, and deploy machine learning pipelines for NLP and multimodal AI solutions. Conduct data preprocessing, feature engineering, and exploratory data analysis for training datasets. Optimize embeddings for semantic search and document retrieval tasks. Model Deployment & Optimization: Deploy ML models in production environments using cloud platforms like … cross-functional teams including data scientists, software engineers, and product managers to align ML solutions with business objectives. Work with data engineers to design feature stores and streaming pipelines. Integrate ML outputs into enterprise systems while ensuring seamless user experiences. Research & Innovation: Stay updated on advancements in generative AI More ❯

teams to understand business requirements and deliver data-driven insights. Design and build scalable data pipelines and ETL processes. Perform data exploration, preprocessing, and feature engineering. Conduct statistical analysis and machine learning model development. Communicate findings and insights to stakeholders through data visualization and reports. Stay current with industry More ❯

fine-tuning, and prompt engineering. ✅ Proficiency in programming languages such as Python, R, and Java. (Python is a must have!) ✅ Strong knowledge of data engineering processes (data cleaning, feature engineering, and integration). ✅ Ability to work independently and as part of a dynamic team. ✅ Experience with Microsoft More ❯

to deliver AI solution blueprints, ensuring alignment with strategic objectives and efficient execution. Qualifications: Educational Background: Bachelor's degree or higher in Computer Science, Engineering, Mathematics, Statistics, or a related field. Management and Technical Expertise: Extensive hands-on experience with platform MLflow and MLOps databricks features. Popular Gen AI … and Snowflake. Domain Knowledge: Strong knowledge of machine learning, deep learning, natural language processing, computer vision, and other AI domains. Data Proficiency: Expertise in feature engineering, data preprocessing, model tuning, and evaluation. MLOps and Cloud Computing: Experience in cloud platforms (Azure, GCP) and their AI/ML services More ❯

tools (R, MATLAB), and associated data science libraries (scikit-learn). Proficiency in data science methods related to data architecture, data munging, data and feature engineering, and predictive analytics. Experience creating meaningful data visualizations and interactive dashboards using platforms such as Tableau, Qlik, Power BI, RShiny, plotly, and … Data technologies (e.g., Hadoop, Spark). Certification in relevant ETL or data integration technologies. Bachelors Degree in data science, mathematics, statistics, economics, computer science, engineering, or other related business or quantitative discipline. Ability to obtain and maintain a Public Trust. CGI is required by law in some jurisdictions to More ❯

Management and Preparation Work with Data Engineers to prepare clean, reliable datasets for analysis and modeling Implement data quality checks and validation procedures Perform feature engineering to optimize model performance Develop efficient data processing pipelines for model training and deployment Document data transformations and analytical methodologies 3. Cross More ❯

as TensorFlow, PyTorch, or Scikit-learn. Expert-level experience in Power BI for advanced visualisations, ML model interpretation, and KPI tracking. Deep knowledge of feature engineering, model deployment, and MLOps best practices. Experience with big data processing (Spark, Hadoop) and cloud-based data science environments. Other: Ability to … integrate ML workflows into large-scale data pipelines. Strong experience in data preprocessing, feature selection, and statistical modelling. Experience communicating complex findings to both technical and non-technical stakeholders. If you are interested and looking for your next role, please apply directly or email . More ❯

as TensorFlow, PyTorch, or Scikit-learn. Expert-level experience in Power BI for advanced visualisations, ML model interpretation, and KPI tracking. Deep knowledge of feature engineering, model deployment, and MLOps best practices. Experience with big data processing (Spark, Hadoop) and cloud-based data science environments. Other: Ability to … integrate ML workflows into large-scale data pipelines. Strong experience in data preprocessing, feature selection, and statistical modelling. Experience communicating complex findings to both technical and non-technical stakeholders. If you are interested and looking for your next role, please apply directly or email (url removed More ❯

mining techniques, with a focus on applications in text analysis or scientific data, including knowledge of forecasting, A/B testing, entity extraction, and feature engineering. Proficiency in programming languages such as Python, R, and SQL, and data analysis libraries (e.g., Pandas, NumPy, SciPy, Tidyverse). Strong knowledge of More ❯

with world-class expertise in machine learning, statistics, optimization and stochastic control. These advisors include AI Labs co-head Stephen Boyd (Samsung Professor of Engineering at Stanford), Emmanuel Candes, Trevor Hastie, and Mykel Kochenderfer who dedicate time in our Palo Alto office and provide advice and guidance for all … problems. Build and maintain tools and services supporting the full model development lifecycle for statistical models, machine learning, optimization, and deep learning models (e.g., feature engineering, backtesting and simulation, validation, deployment). Maintain and monitor production models and experimentation. Tune performance in both single-threaded and distributed environments. … Enforce high-quality patterns and practices for maintaining model pipelines. Requirements 7+ years in software engineering, with 3+ years in API-backed ML deployment. Strong programming language skills in Python. Significant experience with SQL (e.g., RDBMS, Spark, Presto, or BigQuery). Experience with machine learning, optimization, and data manipulation More ❯

and innovate on methods like Bayesian neural networks , variational autoencoders , diffusion models , and Gaussian processes for modern AI use cases. Collaborate closely with product, engineering, and business teams to build end-to-end modeling solutions. Conduct deep-dive statistical and machine learning analyses, simulations, and experimental design. Stay current … programming tools. Hands-on experience with classical ML and modern techniques, including deep learning , transformers , diffusion models , and ensemble methods . Solid understanding of feature engineering, dimensionality reduction, model construction, validation, and calibration. Experience with uncertainty quantification and performance estimation (e.g., cross-validation, bootstrapping, Bayesian credible intervals). … Spark, Pandas). Ability to translate ambiguous business problems into structured, measurable, and data-driven approaches. Preferred Qualifications: M.Sc or PhD in Statistics, Electrical Engineering, Computer Science, Physics, or a related field. Background in generative modeling , Bayesian deep learning , signal/image processing , or graph models . Experience applying More ❯

for end-to-end Machine Learning Scientists that have a strong background and experience in machine learning pipeline going through business understanding, data exploration, feature engineering, model building, performance evaluation, testing and production deployment. Our Product Analytics team is at the heart of data-driven decision-making, empowering … partner closely with product managers and business leaders to uncover insights, measure impact, and drive strategy. By analysing player behaviour, identifying trends, and evaluating feature performance, our team ensures that every decision is informed by robust, actionable data. Together, we fuel innovation and help deliver personalised, engaging experiences that … are looking for people who can support our ethos. To apply to this post, you will have: MSc in Computer Science/Statistics/Engineering or a related field with a focus on applied statistics, AI, machine learning, or related fields with experience working with predictive and probabilistic models More ❯

experiences: Mandatory Experience developing and deploying models or tools using Python. Proficiency with standard statistical and machine learning techniques. Strong understanding of data wrangling, feature engineering, and visualisation techniques. Experience with relational databases and proficiency in SQL. Good communication skills - able to explain technical concepts to non-technical More ❯

to £130,000 | Remote (UK) 167 Solutions Ltd is hiring on behalf of a leading organisation seeking a Senior Data Engineer to join their engineering team. This role is ideal for an experienced data engineer who thrives in a dynamic environment and is eager to design, develop, and maintain … Airflow, AWS Glue, and Amazon Athena . Work with cloud-native technologies to support scalable, serverless architectures. Collaborate with data science teams to streamline feature engineering and model deployment. Ensure data governance, lineage, and compliance best practices. Mentor and support team members in data engineering best practices … . Skills & Experience Required 6+ years of experience in data engineering within large-scale digital environments. Strong programming skills in Python, SQL, and Spark (SparkSQL) . Expertise in Snowflake and modern data architectures. Experience designing and managing data pipelines, ETL, and ELT workflows . Knowledge of AWS services such More ❯

knowledge of Acadian's processes and pertinent new research. Explore structured and unstructured datasets with a focus on data preparation, transformation, outlier detection, and feature engineering. Collaborate on the design of ESG constraints for client-driven investment solutions, help build predictive models and design interactive data applications. We're More ❯

this role, you will provide technical leadership in the development of our Causal AI platform, guiding both the vision and execution within our Product Engineering team, which includes software engineers, data scientists, and machine learning experts. While you will actively contribute to coding and algorithm development, you will also … development of advanced Causal AI algorithms, with a focus on time series and tabular data, ensuring they are optimised for scalability and performance. Overseeing feature engineering and machine learning initiatives to deliver robust, production-quality solutions. Providing mentoring and technical guidance to junior engineers and data scientists, fostering …/UI design, to seamlessly integrate Causal AI capabilities into our platform's architecture. A minimum of 5 years of experience in machine learning engineering or a related field, with demonstrated success in deploying machine learning models into production environments. Strong academic background in a quantitative discipline (e.g., machine More ❯

and MLOps. Required Qualifications: PhD in a relevant discipline or Master’s plus a comparable level of experience Experience with traditional ML models and feature engineering. Experience with LLMs. Strong programming skills (e.g., Python) and experience with modern ML frameworks (e.g., PyTorch, TensorFlow, LangChain). Collaborating with other Researchers … Product, Engineering and Business Stakeholders in an agile manner to demonstrate value and iterate with customer feedback. For immediate consideration, please apply today More ❯