4 Sampling Techniques Jobs

You should have in-depth knowledge of diligence, auditing & accounting standards along with good technical knowledge. It is important to be able to prioritize work on multiple assignments and manage ambiguity effectively. You should have the capability of handling audit assignments independently. Strong verbal and communication skills are essential, along with clarity of thoughts and assertiveness. Good presentation skills and the ability to respond promptly are also required. Strong leadership skills are necessary to deal with senior management and drive various meetings. Requirements include strong knowledge in diligence, auditing, and principles and techniques. You should be proficient in analyzing and performing valuation of complex financial instruments. Designing and implementing internal controls is a key responsibility. You will be expected to perform due diligence and analysis on alternate investment products including products focused on real estate, private debt, etc. Preparing internal due diligence reports from data gathered during the analysis is also part of the role. Organizing and maintaining due diligence filing systems, filing correspondence, and other due diligence related records are important tasks. Performing audits utilizing auditing techniques including risk assessment, audit scoping, devising audit approaches, controls testing, and substantive audit testing including sampling techniques is required. You will interface directly with client management executives and lead teams of junior auditors.,

The ideal candidate for this position should have previous experience in building data science/algorithms based products, which would be a significant advantage. Experience in handling healthcare data is also desired. An educational qualification of Bachelors/Masters in computer science/Data Science or related subjects from a reputable institution is required. With a typical experience of 7-9 years in the industry, the candidate should have a strong background in developing data science models and solutions. The ability to quickly adapt to new programming languages, technologies, and frameworks is essential. A deep understanding of data structures and algorithms is necessary. The candidate should also have a proven track record of implementing end-to-end data science modeling projects and providing guidance and thought leadership to the team. Experience in a consulting environment with a hands-on attitude is preferred. As a Data Science Lead, the primary responsibility will be to lead a team of analysts, data scientists, and engineers to deliver end-to-end solutions for pharmaceutical clients. The candidate is expected to participate in client proposal discussions with senior stakeholders and provide technical thought leadership. Expertise in all phases of model development, including exploratory data analysis, hypothesis testing, feature creation, dimension reduction, model training, selection, validation, and deployment, is required. A deep understanding of statistical and machine learning methods such as logistic regression, SVM, decision tree, random forest, neural network, and regression is essential. Mathematical knowledge of correlation/causation, classification, recommenders, probability, stochastic processes, NLP, and their practical implementation to solve business problems is necessary. The candidate should also be able to implement ML models in an optimized and sustainable framework and gain business understanding in the healthcare domain to develop relevant analytics use cases. In terms of technical skills, the candidate should have expert-level proficiency in programming languages like Python/SQL, along with working knowledge of relational SQL and NoSQL databases such as Postgres and Redshift. Extensive knowledge of predictive and machine learning models, NLP techniques, deep learning, and unsupervised learning is required. Familiarity with data structures, pre-processing, feature engineering, sampling techniques, and statistical analysis is important. Exposure to open-source tools, cloud platforms like AWS and Azure, and AI tools like LLM models and visualization tools like Tableau and PowerBI is preferred. If you do not meet every job requirement, the company encourages candidates to apply anyway, as they are dedicated to building a diverse, inclusive, and authentic workplace. Your excitement for the role and potential fit may make you the right candidate for this position or others within the company.,

You will play a crucial role in developing and maintaining descriptive and predictive analytics models and tools that support Lowe's pricing strategy. Working closely with the Pricing team, you will translate pricing goals and objectives into data and analytics requirements. By leveraging a combination of open source and commercial data science tools, you will gather and wrangle data to provide data-driven insights, trends, and identify anomalies. Utilizing suitable statistical and machine learning techniques, you will address relevant questions and offer retail recommendations. Collaboration with product and business teams is essential to drive continuous improvement, incorporating feedback throughout development to maintain a best-in-class position in the pricing space. Your responsibilities will include translating pricing strategy and business objectives into analytics requirements, implementing processes for data collection and preparation, conducting data validation and outlier detection, designing and implementing statistical and machine learning models, ensuring model accuracy, and applying machine learning outcomes to business use cases. Educating the pricing team on interpreting and utilizing machine learning model results, designing and executing experiments to evaluate price changes, performing advanced statistical analyses, and managing analytics projects are key aspects of this role. You will also lead the exploration of new analytical techniques, collaborate with various teams, stay updated on industry developments, and mentor junior analysts to foster skill development and team growth. With 6-12 years of relevant experience, you should hold a Bachelor's or Master's degree in Engineering, Business Analytics, Data Science, Statistics, Economics, or Mathematics. Your skill set should include expertise in advanced quantitative analysis, statistical modeling, and machine learning models, along with experience in regression, hypothesis testing, time series analysis, predictive modeling, and other analytical concepts. Proficiency in SQL, Python, R, SAS, enterprise-level databases, data visualization tools, and cloud platforms is required. Technical expertise in Alteryx and Knime is desired for this role.,

Conduct physical verification and tagging of inventory and fixed assets, ensure data accuracy, reconcile records, identify discrepancies, support audit processes, and assist in maintaining updated asset registers for compliance and reporting.