years leading Databricks-based solutions. Proven experience in a consulting environment delivering large-scale data platform projects. Hands-on expertise in Spark, Delta Lake, MLflow, Unity Catalog, and DBSQL. Strong proficiency in Python, SQL, and at least one major cloud platform (AWS, Azure, or GCP). Excellent communication skills and More ❯
Data Science Applications. Proficient Python skills, including experience with relevant data libraries. Cloud engineering experience, particularly with AWS and Databricks.Exposure to GenAI/NLP, MLflow, Jenkins, workflow automation, AutoML, unit testing, and model explainability is a plus What You'll Do: As a Machine Learning Engineer, you will be a More ❯
model deployments. Proficient Python skills, including experience with relevant data libraries. Cloud engineering experience, particularly with AWS and Databricks. Exposure to GenAI/NLP, MLflow, Jenkins, workflow automation, AutoML, unit testing, and model. What You'll Do: As a Machine Learning Engineer, you will be a pivotal part of our More ❯
applying probabilistic models in real-world applications (e.g., recommendation systems, anomaly detection, personalized healthcare, etc.). Understanding of modern ML pipelines and MLOps (e.g., MLFlow, Weights & Biases). Experience with recent trends such as foundation models , causal inference , or RL with uncertainty . Track record of publishing or presenting work More ❯
tools. Ability to communicate complex ideas in machine learning to non-technical stakeholders. You may have: Experience with one or more ML Ops frameworks - MLFlow, Kubeflow, Azure ML, Sagemaker. Strong theoretical foundations in linear algebra, probability theory, or optimization. Experience and training in finance and operations domains. Deep experience with More ❯
decision-making processes Hands-on experience with AI/ML frameworks (TensorFlow, PyTorch), LLM orchestration tools (LangChain, LangGraph), MLOps practices and tooling (such as MLflow, Kubeflow, or similar), vector databases, and cloud platforms (AWS, Azure, GCP) with their AI/ML offerings Preferably hands-on experience with voice technologies and More ❯
teams. Deep knowledge of media measurement techniques, such as media mix modelling. Experience with advanced AI techniques, including NLP, GenAI, and CausalAI. Familiarity with MLFlow, API design (FastAPI), and dashboard building (Dash). If this role looks of interest, reach out to Joseph Gregory More ❯
teams. Deep knowledge of media measurement techniques, such as media mix modelling. Experience with advanced AI techniques, including NLP, GenAI, and CausalAI. Familiarity with MLFlow, API design (FastAPI), and dashboard building (Dash). If this role looks of interest, reach out to Joseph Gregory More ❯
teams. Deep knowledge of media measurement techniques, such as media mix modelling. Experience with advanced AI techniques, including NLP, GenAI, and CausalAI. Familiarity with MLFlow, API design (FastAPI), and dashboard building (Dash). If this role looks of interest, reach out to Joseph Gregory More ❯
/GCP. · Ability to manage cloud infrastructure to ensure high availability, scalability, and cost efficiency. Nice-to-Have · Experience with ML orchestration platforms like MLflow, SageMaker Pipelines, Kubeflow, or similar. · Familiarity with model quantization, pruning, or other performance optimization techniques. · Exposure to distributed training frameworks like Unsloth, DeepSpeed, Accelerate, or More ❯
/GCP. · Ability to manage cloud infrastructure to ensure high availability, scalability, and cost efficiency. Nice-to-Have · Experience with ML orchestration platforms like MLflow, SageMaker Pipelines, Kubeflow, or similar. · Familiarity with model quantization, pruning, or other performance optimization techniques. · Exposure to distributed training frameworks like Unsloth, DeepSpeed, Accelerate, or More ❯
with marketing data or customer-level modelling (e.g., uplift, attribution, causal AI, graph AI, campaign optimization, spend optimization). Exposure to MLOps tools like MLflow, FastAPI, Airflow, or similar. Experience with experimentation and validation frameworks (e.g., A/B testing). Startup or freelance experience that required pace, clarity, and More ❯
learning models into production, including managing their lifecycle Experience implementing model governance e.g. model versioning, drift reporting etc. Experience with MLOps tools such as MLFlow, Kubeflow, or DVC Experience with distributed processing systems like Spark (Scala and PySpark would be invaluable) Experience in programming with Scala Experience with LLMs, and More ❯
observability principles, including monitoring, logging, and real-time system insights. AI/ML Lifecycle Awareness : Familiarity with the machine learning lifecycle (e.g., tools like MLflow) and its integration into production systems. Collaboration : Strong interpersonal skills with the ability to work effectively across teams, including specialists in security and data science. More ❯
with unit and integration tests Strong understanding of machine learning algorithms and best practices Vision for MLOps best practices, particularly regarding version control, Docker, MLFlow, CI/CD Strong communication skills, with the ability to engage effectively with diverse stakeholders Good commercial understanding; knowledge of marketing operations is a bonus More ❯
while fostering an inclusive and high-performing culture. Strong hands-on background in deploying machine learning models at scale, using technologies such as Pytorch, MLflow, Airflow, and Docker. Deep understanding of ML lifecycle challenges: feature stores, model deployment, monitoring, data drift, and retraining. Familiarity with cloud platforms (ideally Azure), infrastructure More ❯
Evidently, etc.) Experience in building parallelised or distributed model inference pipelines Nice-to-Have Skills Familiarity with feature stores and model registries (e.g. Feast, MLflow, SageMaker Model Registry) Knowledge of model versioning , A/B testing , and shadow deployments Experience implementing or contributing to MLOps frameworks and scalable deployment patterns More ❯
pressure Nice-to-Have: Experience with marketing data or customer-level models (e.g. uplift, attribution, causal inference, campaign optimization) Familiarity with MLOps tools (e.g. MLflow, FastAPI, Airflow) Exposure to A/B testing and experimentation frameworks WHY THIS ROLE IS DIFFERENT This isn’t a narrow data science role — you More ❯
pressure Nice-to-Have: Experience with marketing data or customer-level models (e.g. uplift, attribution, causal inference, campaign optimization) Familiarity with MLOps tools (e.g. MLflow, FastAPI, Airflow) Exposure to A/B testing and experimentation frameworks WHY THIS ROLE IS DIFFERENT This isn’t a narrow data science role — you More ❯
audiences Nice-to-Have: Experience with marketing data, customer-level modelling, or decision science (e.g. uplift, attribution, causal AI, optimization) Familiarity with MLOps tooling (MLflow, FastAPI, Airflow, etc.) Experience designing and interpreting A/B tests or other experimental frameworks Background in consulting, agency, or fast-paced environments where autonomy More ❯
audiences Nice-to-Have: Experience with marketing data, customer-level modelling, or decision science (e.g. uplift, attribution, causal AI, optimization) Familiarity with MLOps tooling (MLflow, FastAPI, Airflow, etc.) Experience designing and interpreting A/B tests or other experimental frameworks Background in consulting, agency, or fast-paced environments where autonomy More ❯
Experience with business intelligence tools like Tableau or PowerBI. Experience working with LLMs. Experience working with AWS Services like EC2, RDS(Postgres), SQS, Sagemaker, MLflow, S3, API gateway, ECS. Experience in UI frameworks like VueJS is a plus. About Us FactSet creates flexible, open data and software solutions for tens More ❯
