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    Software Engineer, Python, Remote - London, United Kingdom - Cerberus Capital Management

    Cerberus Capital Management
    Cerberus Capital Management London, United Kingdom

    2 weeks ago

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    Description
    Machine Learning Engineer at Cerberus Capital Management

    As a machine learning engineer on our CTS team, you will contribute to the firm's objectives by designing, implementing and deploying tools and solutions for a broad range of business objectives, such as asset pricing, demand forecasting, sentiment analysis, and other machine-learning techniques for pattern recognition and statistical modeling.

    Cerberus Capital Management (CCM) is a private equity firm with partial or full ownership stakes in over 40 companies in a variety of industries.

    The CTS teams include Data Science, Data Management and Client Engagement, which work closely together with clients to identify business opportunities and create new business value through improved data handling and analysis.

    Build predictive models using machine-learning techniques that generate data-driven insights on modern data platforms (Spark, Hadoop and other map-reduce tools);
    Develop and productionalize containerized algos for deployment in hybrid cloud environments (GCP, Azure)
    Connect and blend data from various data sources within enterprise tools (python, pandas, or SQL) to enable application of Data Science methods
    Create metrics and analytical reports to ensure data quality and business value. Clean, structure and normalize data to eliminate redundant or unnecessary information to enable robust and sound analysis
    Participate in the development of both back-end data pipelines and front-end applications
    Evaluate 3rd party solutions for functionality, quality and applicability to client use cases.

    6+ Years of experience in an engineering role with a degree in Mathematics, Engineering, Statistics, Computer Science or Physics.

    Knowledge of Linear Algebra, Probability Theory, Statistics and Optimization, including regression analysis, parameter estimation, factors selection, PCA, hypothesis testing, time series, queuing theory, survival analysis, clustering, linear programming.

    ~ Experience with machine learning methods, such as regularization, random forests, neural networks and deep learning.
    ~ Ability to write algorithms and implement pipelines in Python. Experienced in SQL. Familiarity with various relational database platforms is a plus (SQL Server, MySql, PostgreSQL, Oracle, Snowflake, Vertica, etc). Familiarity with DevOps process for model deployment and unit testing.
    ~ Experience of work in collaborative development environment (GIT, Azure DevOps, JIRA).
    ~ Ability to present ideas and solutions in business-friendly and user-friendly language to colleagues, management and clients.

    Established in 1992, Cerberus Capital Management, L.together with its affiliates, is one of the world's leading private investment firms.

    Through its team of investment and operations professionals, Cerberus specializes in providing both financial resources and operational expertise to help transform undervalued and underperforming companies into industry leaders for long-term success and value creation.

    The Firm's proprietary operations team, , employs world-class operating executives to support Cerberus' investment teams in the following areas: sourcing opportunities, conducting highly informed due diligence, taking interim management roles, monitoring the performance of investments and assisting in the planning and implementation of operational improvement initiatives at Cerberus' portfolio companies.

    Cerberus Technology Solutions is an operating company and subsidiary of Cerberus Capital Management focused exclusively on leveraging emerging technology, data, and advanced analytics to drive transformations.

    Our expert technologists work closely with Cerberus investment and operating professionals across our global businesses and platforms on a variety of operating initiatives targeted at improving systems and generating value from data.

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