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Experience developing and deploying Machine Learning solutions on cloud platforms (e.g., AWS, Azure, or Google Cloud Platform). 8+ years of experience in developing business applications for Machine Learning and Data Science workloads.
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Expertise in at least two fields in Natural Language processing (Natural Language understanding, deep learning, Natural Language Generation, Large Language Models, and generative AI) or at least two fields in Data Engineering (ETL data pipeline, big data processing, data warehouse, NoSQL database, distributed/parallel systems, and cloud computing.
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Contributing to the continuous improvement of our cybersecurity strategies through innovative data science approaches, staying abreast of the latest in machine learning and cybersecurity trends.
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Proven experience in data science, machine learning, or a related role, with a strong preference for experience in cybersecurity. Certifications such as Certified Information Systems Security Professional (CISSP), Certified Data Privacy Solutions Engineer (CDPSE), GIAC Machine Learning Engineer (GMLE) are highly desirable.
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Four or more years of Data Science experience in Python-based Data Science stack: Scikit-Learn, TensorFlow, PyTorch, Pandas, NumPy, Polars, Matplotlib, Pyspark. Familiarity with big data technologies and platforms (Dask, BigQuery, Kafka, Spark Structured Streaming.
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If the candidate does not have a background in Actuarial science, the candidate should have 4+ years of industry experience building and analyzing machine learning models and should hold a graduate degree in a technical field such as Statistics, Computer Science, Data Science, Bioinformatics, Physics, Mathematics, Economics or Engineering.
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We are seeking Full Stack Software Engineers who are passionate about marrying data with emerging technologies. 2+ years of experience with OpenSearch, Kafka, Vue.js and some exposure to Machine Learning.
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Full Stack Development experience, including experience with Python, Django web applications, REACT web applications or other Javascript frameworks, RESTful API architecture, and incorporating machine learning models into web apps.
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Modern Data Stack experience (Snowflake, Synpase, Databricks, Redshift, DBT, and/or Airflow) 10 years of experience in data engineering, data architecture, BI & Analytics, and/or Data Science.
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At least 1 year experience with cloud computing (AWS, Microsoft Azure, Google Cloud) Lead a portfolio of diverse technology projects and a team of developers with deep experience in distributed microservices, and full stack systems to create solutions that help meet regulatory needs for the company.
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Lead and participate in solutioning sessions, working hand-in-hand with businesses/clients, translating business objectives into actionable machine learning/data science use casesWhat You'll Bring.
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Must have knowledge of advance data science methods; examples include prescriptive, cognitive algorithms, big data analytics, commercial and open source Hadoop, data-mining, Python, “R”, machine learning, or NLP.
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In our Data science track we prepare you to get job as one of the following: Python developer, a data analyst, data visualization developer, a statistician, a machine learning engineer or a data scientist.
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Hands on experience with modern cloud data warehouse and engineering for one or more of the following cloud providers - GCP (Big Query, Vertex, Cognitive Services), Snowflake, Azure (Synapse, Dynamo, Cosmos, Azure ML, Power BI), AWS (Redshift, Sage maker), Tableau;Possessing a certification(s) in one of the following is a big plus – GCP/AWS/Azure/ data or ML engineering.
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As a Senior Reliability Engineer, you will play a critical role in ensuring the robustness, availability, and performance of our cutting-edge Data Engineering and Machine Learning Platforms.
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