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3+ years of hands-on experience in statistical process control and data analysis, using tools such as JMP, Tableau, Matlab, or equivalent. Expertise in and understanding of manufacturing and operational fundamentals, such as Quality Control Plans (QCP), Statistical Process Control (SPC), Change Control Board (CCB), Material Review Board (MRB), Machine and Process Buy-off (MBO/PBO), pFMEA, overall equipment efficiency (OEE), units per hour (UPH), etc.
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As The Director, Statistical Programming, you will be a champion of Otsuka's culture and values and serve in a leadership role within the Statistical Programming group. Serve as the in-house expert and key advisor to head of the Statistical Programming group on the programming strategy by providing assessment of state-of-art and industry trends in ways of working, efficient coding such as macro library and utility macros, and technology.
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Knowledge of statistics and experience using statistical packages for analyzing datasets (Excel, SAS, R, SPSS, etc.) Meeting with analysts, customers, and partners to showcase value and drive adoption of the resource capacity planning processes and the Clarity PPM tool.
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Demonstrates knowledge of post-educational, private sector work in the financial services or bank regulatory areas required, including: Prepayment, credit risk, fair lending, default, Basel or fair lending modeling/modeling validation, advanced statistical and/or econometric modeling techniques and complex Excel-based spreadsheet models Statistical modeling assessing strengths and weaknesses.
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Demonstrates thorough statistical and/or econometric modeling systems and tools, including Excel based spreadsheet models, SAS programming skills, additional experience with Visual Basic, SQL, or C.
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Experience with display component or module assembly processes, such as SMT, lamination, screen-printing, laser cutting, edging, molding, 3D printing. Experience with materials, such as adhesives (e.g. PSA, OCA, LOCA, glue), optical coatings, ophthalmic lenses, electrochemical cells.
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OR Master's degree in genetics or statistical genetics; pharmacology; biology (particularly molecular, evolutionary, or computational biology); biostatistics; bioinformatics; data mining; biomedical/healthcare engineering, or a comparable degree/training is required.
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Candidates with skills in data management and analysis (R, Python), computer science (R, Python, Linux) and statistical genetics (GWAS, pheWAS, WGS, etc.) There will also be opportunities for attendance of statistical genomics training workshops.
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Preferred proficiency with spreadsheets (e.g., excel/lotus), data bases (e.g., Access, Dbase), data collection and survey design platforms (e.g., Qualtrics), statistical software (e.g., SPSS, SAS), qualitative data analysis software (e.g., NVIVO, MAXQDA), advanced software (e.g., AMOS, MixORD), and analysis of repeated measures/longitudinal data as appropriate for area of research expertise.
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Experience with statistical software preferred (e.g., SPSS, SAS, R, Strata) preferred. The RPI Specialist will utilize (Plan-Do-Check-Act) PDCA, Lean, and Six Sigma improvement methodologies, robust data/statistical analytics, as well as Change Management in improvement work.
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1+ years of experience in advanced data analysis (using JMP, GAJT and/or SQL), statistical process control (SPC), and design of experiment (DOE) principles. Develops solutions and solves problems utilizing formal education, experience, statistical analysis, and problem-solving methodologies / tools.
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Strong study design and statistical analysis skills with proficiency in one are more statistical applications (SAS/R/Python) is required. Work independently to lead and/or execute study plans and statistical analysis for assigned projects.
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Advanced knowledge of materials, manufacturing processes (e.g., plastic injection molding, machining, anodizing, soldering, automated assembly), sterilization, cable interconnect assemblies, bulk cable design principles, and advanced statistical techniques.
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Skilled in data visualization and use of graphical applications, including Microsoft Office (Power BI) and Tableau; major data science languages, such as R and Python; managing and merging of disparate data sources, preferably through R, Python, or SQL; statistical analysis; and data mining algorithms.
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Additional Skills (Advantageous): Familiarity with web analytics platforms such as Google Analytics or Mixpanel, proficiency with BI tools (e.g., Tableau, Power BI), statistical packages (e.g., Stata, SPSS, "R"), and some programming background (e.g., Python, SQL, VBA.
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statistical job
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