Senior Data Scientist - U.S. Citizenship Required at Ardent MC

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Senior Data Scientist - U.S. Citizenship Required Ardent MC. . Why do you need to choose between doing important work and having a fulfilling life? . At Ardent. , we have both. Ardent employees are committed to solving our customers’ most difficult problems—and we are committed to the well-being, personal goals, and professional development of our employee. We are “All In.” We put forth our strongest effort possible to get the mission accomplished and we do it together. We respect the skills and experience you bring to the Ardent team. And we provide a rewarding environment to help you succeed. . . We offer highly competitive benefits, professional development opportunities, and an exceptional culture that embraces flexibility, innovation, collaboration, and career growth. A collective service mindset underpins our work, and a shared camaraderie to serve clients, colleagues and our communities set us apart. Our full commitment to being "All In" for our employees and our clients is not just our approach, it is our standard. If this sounds like the perfect fit for you, choose Ardent and make a difference with us. . . Ardent. is seeking a . Sr. Data Scientist . to join our team. . . This is a . Remote position. .. . Position Description:. . Ardent. is seeking a . Senior Data Scientist . to join our team. The ideal candidate will possess expertise in various machine learning techniques, particularly in the areas of time series forecasting, reinforcement learning, computer vision, predictive maintenance, NLP, anomaly detection, and domain-specific knowledge in digital twins use cases.. . Responsibilities and Duties:. . . Utilize methods such as ARIMA, Prophet, and LSTM neural networks to predict expected sensor values, state changes, and events.. . Uncover patterns over time to estimate future twin model behavior.. . Develop algorithms to dynamically determine optimal control policies and simulation parameters to achieve specified goals.. . Calibrate digital twin responses through reinforcement learning techniques.. . Analyze visual data from cameras and imagery to evaluate system conditions.. . Apply skills in object detection and image segmentation to derive visual insights.. . Employ supervised regression algorithms to estimate time-to-failure and fault likelihood based on equipment age, lifetime parameters, and sensor indicators from the physical twin.. . Utilize NLP techniques to analyze textual and unstructured data for valuable insights.. . Develop unsupervised ML models to learn expected data variance and identify outlier sensor readings requiring investigation.. . Detect early performance issues through anomaly detection methods.. . Apply domain-specific knowledge in digital twins use cases to optimize modeling and analysis.. . . Requirements: . . . Bachelor's degree or higher in Computer Science, Data Science, Engineering, or related field.. . Proven experience in applying machine learning techniques in real-world applications, preferably in the areas of time series forecasting, reinforcement learning, computer vision, predictive maintenance, NLP, and anomaly detection.. . Proficiency in programming languages such as Python or R.. . Strong analytical and problem-solving skills.. . Excellent communication and teamwork abilities.. . Experience with relevant tools and libraries (e.g., TensorFlow, . PyTorch. , scikit-learn, OpenCV).. . Familiarity with data visualization techniques and tools.. . Ability to work independently and collaborate effectively in a team environment.. . Prior experience with digital twins use cases is a plus.. . . Due to the nature of the work we support, all candidates in consideration for this role must be U.S. Citizens willing to undergo the government issued background investigation process.. . Ardent . is an equal opportunity employer. We will not discriminate and will take affirmative action measures to ensure against discrimination in employment, recruitment, advertisements for employment, compensation, termination, upgrading, promotions, and other conditions of employment against any employee or job applicant on the bases of race, color, gender, national origin, age, religion, creed, disability, veteran's status, sexual orientation, gender identity or gender expression.. .