• responsible for all aspects of clinical data management associated with data cleaning and quality review processes for assigned projects with minimal supervision. • ensure clinical trial data collected meet the highest standards of data integrity, while meeting timelines and budgets. • support CRF design, review and validation of clinical database, including supporting management of CRO activities in this area if required; • responsible for creation of data management plans and other data management documentation as needed with minimal oversight; • monitor progress and conduct of their respective projects, including all data cleaning and QC activities to ensure all remain on target to project timelines; • participate in ongoing data review throughout the conduct of the study, including being responsible for the correction of errors and discrepancies management for the life of a project; • proactively identify and address issues that may impact the quality of the data, deliverables or timelines; • responsible for handling of non-CRF data, including lab data and image handling; • responsible for co-ordinating with Medical Affairs organization to facilitate data coding and safety reviews as needed; • contribute towards process improvement, data standards and efficiency gaining initiatives within data management working with data management leadership as needed;

• Solve complex business challenges through advanced analytics, machine learning, and AI • Collaborate with business leaders to understand strategic problems • Build and tune predictive or NLP models • Design data pipelines to support scalable solutions • Translate analytical findings into clear recommendations for clients and stakeholders • Work with large, complex datasets and connect data from multiple sources • Develop production-ready models that drive measurable business outcomes • Serve as a trusted advisor and leader across the organization • Partner closely with cross-functional teams to shape data strategy • Present findings to technical and non-technical audiences and mentor junior data scientists.

Role Description As an Associate Data Scientist, you'll help turn complex data into insights that drive real business decisions. Working alongside experienced data scientists, engineers, and business leaders, you'll support projects that help utility clients operate more efficiently, make informed decisions, and solve meaningful challenges. - Explore datasets and uncover trends. - Build and test machine learning models. - Work with data from a variety of sources—cleaning, organizing, and preparing it for analysis. - Use tools like Python and SQL to explore patterns and answer business questions. - Support the development of analytical and predictive solutions. - Create dashboards, visualizations, and reports for both technical and non-technical audiences. This is a hands-on learning opportunity where curiosity and growth are encouraged. You'll collaborate with experienced professionals who will mentor and guide you as you strengthen your technical skills, learn new tools and technologies, and gain exposure to the full data science lifecycle—from data preparation and analysis to machine learning and business storytelling. If you enjoy solving problems, asking questions, and continuously learning, you'll find plenty of opportunities to develop your career here while making a meaningful impact from day one. Qualifications - Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, Mathematics, or a related field. - 0–2 years of relevant experience, including internships, academic projects, or research experience. - Foundational knowledge of Python for data analysis and modeling. - Basic understanding of SQL and relational databases. - Familiarity with machine learning concepts and statistical analysis. - Experience with data visualization tools or libraries such as Tableau, Matplotlib, Seaborn, or Plotly. Requirements - Experience with data science libraries such as Pandas, NumPy, and Scikit-learn. - Understanding of machine learning workflows and model evaluation techniques. - Exposure to working with structured and unstructured data. - Coursework or experience in natural language processing (NLP), cloud platforms (such as Azure), or big data technologies. - Strong analytical, problem-solving, and communication skills. - A demonstrated desire to learn, collaborate, and grow in a fast-paced, team-oriented environment. Benefits - A supportive and inclusive work environment that values diversity and encourages innovation. - Opportunities for professional growth and career development. - Competitive salary and comprehensive benefits package, including generous paid time off, 401(k) with company match, tuition reimbursement, and flexible work schedules. - This position pays between $62,400 to $83,300 annually and is an exempt position. - Benefits offered include company-sponsored medical, dental, vision, and 401(k) plan with company match. - Also offered are tuition reimbursement, voluntary life, disability, critical illness, accident and hospitalization plans. - HSA or FSA medical accounts and FSA dependent care and transportation/parking accounts are also available. - These benefits are subject to employee contribution amounts generally set annually. - Full-time employees are eligible to earn PTO hours. - May be eligible for discretionary bonus as determined by the company.

• Perform advanced data analyses to generate strategic insights and support decision-making • Develop Machine Learning, Deep Learning, and NLP models applied to business challenges • Work end-to-end in the data lifecycle, from ingestion and preprocessing to deployment and monitoring of models in production • Support the design and maintenance of data pipelines (ETL/ELT), ensuring data quality, scalability, and reliability • Develop, validate, and deploy predictive models to production • Work with large volumes of data, optimizing performance and processing • Integrate analytical solutions into systems and corporate applications • Implement continuous model monitoring, tracking accuracy, drift, and performance metrics • Automate analytical processes and data pipelines • Collaborate with multidisciplinary teams in engineering, product, and business to ensure real impact on company initiatives.