**Company: McKinsey & Company Job Description Summary: The text discusses the qualifications required for a Data Scientist position at QuantumBlack in Tokyo, which is part of McKinsey & Company. The ideal candidate should have a bachelor’s, master’s, or PhD in a relevant discipline, programming experience in Python, good communication skills, experience with data science and machine learning methods, and fluency in both Japanese and English. The role involves working on interdisciplinary projects with a diverse team and utilizing data to provide real-world impact for various industries globally. Job Description: Who You’ll Work With You will be based in Tokyo and will be part of the QuantumBlack team. You will work with other integrative consultants, data scientists, data engineers, machine learning engineers, designers and project managers on interdisciplinary projects, using math, stats and machine learning to derive structure and knowledge from raw data across various industry sectors. Who you are You are a highly collaborative individual who is capable of laying aside your own agenda, listening to and learning from colleagues, challenging thoughtfully and prioritizing impact. You search for ways to improve things and work collaboratively with colleagues. You believe in iterative change, experimenting with new approaches, learning and improving to move forward quickly. What You’ll DoAs a Data Scientist at QuantumBlack in Tokyo office you will work in multi-disciplinary environments harnessing data to provide real-world impact for organizations globally. You will influence many of the… recommendations our clients need to positively change their businesses and enhance performance. Role and responsibilities • Work on complex and extremely varied data sets from some of the world’s largest organizations to solve real world problems • Develop data science products and solutions for clients as well as for our data science team • Write highly optimized code to advance our internal Data Science Toolbox • Work in a multi-disciplinary environment with specialists in machine learning engineering and design • Add real-world impact to your academic expertise, as you are encouraged to write ‘black’ papers and present at meetings and conferences should you wish • Attend conferences such as NEURIPS and ICML as one global team as well as Data Science retrospectives where you will have the opportunity to share and learn from your co-workers • Work within one of the largest and most advanced data science teams in London, support the lead data scientists to develop data science products What you’ll learn • How successful projections on real world problems across a variety of industries are completed through referencing past deliveries of end to end machine learning pipelines • Build products alongside the core engineering team and evolve the engineering process to scale with data, handling complex problems and advanced client situations • Be able to focus on modelling by working alongside the Data Engineering team which focuses on the wrangling, clean-up and transformation of data • Best practices in software development and productionize machine learning by working with our machine learning Engineering teams which optimize code for model development and scale it • Work with our UX and Visual Design teams to interpret your complex models into stunning and user-focused visualizations • Using new technologies and problem-solving skills in a multicultural and creative environment You will work on the frameworks and libraries that our teams of Data Scientists and Data Engineers use to progress from data to impact. You will guide global companies through data science solutions to transform their businesses and enhance performance across industries including healthcare, automotive, energy and elite sport. Real-World Impact– No project is ever the same; we work across multiple sectors, providing unique learning and development opportunities internationally. Fusing Tech & Leadership – We work with the latest technologies and methodologies and offer first class learning programs at all levels. Multidisciplinary Teamwork – Our teams include data scientists, engineers, project managers, UX and visual designers who work collaboratively to enhance performance. Innovative Work Culture– Creativity, insight and passion come from being balanced. We cultivate a modern work environment through an emphasis on wellness, insightful talks and training sessions. Striving for Diversity– With colleagues from over 40 nationalities, we recognize the benefits of working with people from all walks of life. Our projects range from helping pharmaceutical companies bring lifesaving drugs to market quicker to optimizing a Formula1 car’s performance. At QuantumBlack you have the best of both worlds; all the benefits of being part of one of the leading management consultancies globally and the autonomy to thrive in a fast growth tech culture: Healthcare Efficiency– We helped a healthcare provider improve their clinical trial practices by identifying congestion in diagnostic testing as a key indicator of admissions breaches. Environmental Impact– We designed and built the first data-driven application for a state of the art center of excellence in urban innovation by collecting real-time data from environmental sensors across London and deploying proprietary analytics to find unexpected patterns in air pollution. Product Development– We worked with the CEO of an elite automotive organization to reduce the 18-month car development timeframe by improving processes, designs and team structures. Please submit your CV in English. Visit our Careers site to watch our video and read about our interview processes and benefits. As an equal opportunity employer, QuantumBlack encourages applications from all backgrounds regardless of gender, race, disability, pregnancy, marital status, age, sexual orientation, gender reassignment, religion or belief. We maintain a sense of community rooted in respect and consideration for all employees where any evaluation is based simply upon individual work and team performance. Qualifications • Bachelor’s, master’s or PhD level in a discipline such as: computer science, machine learning, applied statistics, mathematics or engineering • Programming experience; Python is essential, other languages (e.g. R and SQL) are beneficial • Good presentation and communication skills, with the ability to explain complex analytical concepts to people from other fields • A methodical yet creative problem solver • Experience in applying data science and machine learning methods to business/real world problems • Fluency in Japanese (JLPT N1 level or equivalent for non-native Japanese speakers) and business level English
Company:McKinsey & Company
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Level of experience (years):Senior (5+ years of experience)
“Fine-tuning” means adapting an existing machine learning model for specific tasks or use cases. In this post I’m going to walk you through how you can fine tune a large language model for sentence similarity using some hand annotated test data. This example is in the psychology domain. You need training data consisting of pairs of sentences, and a “ground truth” of how similar you want those sentences to be when you train your custom sentence similarity model.
Hire an NLP developer and untangle the power of natural language in your projects The world is buzzing with the possibilities of natural language processing (NLP). From chatbots that understand your needs to algorithms that analyse mountains of text data, NLP is revolutionising industries across the board. But harnessing this power requires the right expertise. That’s where finding the perfect NLP developer comes in. Post a job in NLP on naturallanguageprocessing.
Natural language processing What is natural language processing? Natural language processing, or NLP, is a field of artificial intelligence that focuses on the interaction between computers and humans using natural language. NLP is a branch of AI but is really a mixture of disciplines such as linguistics, computer science, and engineering. There are a number of approaches to NLP, ranging from rule-based modelling of human language to statistical methods. Common uses of NLP include speech recognition systems, the voice assistants available on smartphones, and chatbots.
