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Experience with Databricks for data engineering, analytics, and machine learning. Utilize Databricks for data engineering, analytics, and machine learning tasks.
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Position Summary: The Senior Manager of Data Analytics predominately uses a tech stack of Power BI, SQL, Visual Studio Code, Snowflake, and Excel to answer questions to data that is ETL’d from our ERP, CRM, and various internal and external data sources.
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What You'll DoProcess, cleanse, and verify the integrity of data used for downstream analytics productsApply advanced machine learning, graph analytics, data mining, statistical methods, algorithms, and time series models to vast amounts of user data to derive insights based on the knowledge graph (ontology)Participate in strategic planning and be responsible for the team roadmap and its execution.
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Machine Learning | AI | Python | SQL | Deep Learning | Data Modeling | RAG | NLP | LLM | Start Up | Recommendation Systems. As a Machine Learning Engineer ( Data Extraction ), you can expect to earn up to $210,000 (depending on experience), highly competitive benefits and equity.
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Apply machine learning, econometrics/statistics, predictive modeling, return-on-investment analysis, simulation, and data visualization methods to support the development of health policy.
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Understanding of database technology and experience with a cloud SQL data warehouse (e.g. BigQuery, Postgres, Snowflake) Whether it’s holding a PhD-level deep dive into understanding fairness and underlying bias in machine learning models, debating the merits of a Scandinavian design philosophy in our UI/UX, or writing responses for Medicare rules to influence U.S. health policy, we prioritize sharing our findings across the team and helping each other be successful.
$165,000 - $200,000 a yearFull-timeExpandApply NowActive JobUpdated Today - UpvoteDownvoteShare Job
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Databricks Machine Learning Certificate Preferable. Azure Platform Experience: The candidate should have hands-on experience with various Azure services including but not limited to Azure Data Factory, Databricks, Data Lake, and Power BI. They should be able to design, build, and maintain ETL pipelines, manage data lakes, and create insightful reports and visualizations.
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Excellent technical ability, including proficiency with Python, SQL, Excel, and data visualization tools (Tableau, Power BI, etc.) Excellent understanding of machine learning techniques and algorithms such as XGBoost, random forests, logistic regression, k-means clustering, PCA etc.
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Understand and apply concepts associated with data modeling, machine learning, and artificial intelligence (AI) and their application in client workflows. Become familiar with common data collection, cleaning, analysis, and visualization technologies including, but not limited to, Python, R, SQL, Cypher, Power BI, Tableau, etc.
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Utilize Databricks for data processing, analytics, and machine learning tasks. The ideal candidate will have a strong background in SQL Server development, hands-on experience with Azure Pipeline, and proficiency in working with Databricks for data processing and analytics.
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Experience applying a range of analytic approaches, including (but not limited to) machine learning, text analytics, and natural language processing; graph theory, link analysis and optimization models; complex adaptive systems; and/or deep learning neural networks that are part of the exploration.
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Proficiency in Python, R, SQL and machine learning frameworks such as TensorFlow, PyTorch, or similar. We seek a Data Scientist to work closely with our Director of Applied AI to develop and implement advanced data analytics and machine learning solutions to support the firm’s strategic goals.
$115,000 - $143,000 a yearFull-timeExpandApply NowActive JobUpdated Today - UpvoteDownvoteShare Job
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The Enterprise Model Platform team in the Machine Learning Experience (MLX) organization supports a hosted internal Jupyter Notebook system running critical infrastructure for modeling and exploratory analysis across Capital One. You will get an opportunity and exposure to the thousands of models being trained on the notebook platform by myriad users across Capital One and dive deep into the unique engineering challenges we have around data access, security and scale.
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Strong technical proficiency with data analysis/analytics tools and platforms including GCP, Snowflake, Tableau or other data visualization tools, and Mixpanel or other product analytics tools Experience building and maintaining data pipelines in production Expertise in SQL with a proven ability to write high-quality, efficient SQL queries Familiarity with AI technologies such as machine learning models and natural language processing to analyze and interpret complex data sets.
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The ideal candidate will possess a strong background in data analysis, statistical modeling, and machine learning. Proficiency with data science tools and programming languages such as Python, R, SQL, and Scala.
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