ジョブID

13566

勤務地

Shiroyama Trust Tower 20F, 4-3-1 Toranomon, Minato-ku

職種

正社員

給与形態

年収

給与額

5,000,000円 - 8,000,000円

職種

IT

職務明細

At nanameue, Inc., our focus is to connect people through social media. nanameue, Inc. started in May 2013 with roots in Japan and Thailand. We have built one of Japan’s top chat applications with 9M+ registered users and 75,000+ communities and growing.
nanameue, Inc. is built with people from a variety of different cultures from Japan, Thailand, Malaysia, Sri Lanka, and Australia to name a few. To maintain the global culture, our company language is English, and we continue to welcome new members worldwide.
Our goal is to build a virtual space where everyone can have their presence as themselves and welcome everyone as they are.
As a Data Scientist, you will work with highly complex structured and unstructured datasets. Yes, we are talking terabytes. You will build Statistical, Machine
Learning, and/or Deep Learning models to create solutions, improve operation, understand & interpret the data and be a building member of our SNS platform Yay!.

スキル・資格

• 3+ years experience in data science: data visualization, regression, clustering and classification
• Fluent in Python (Numpy, Pandas, Scikit-Learn)
• Experience working with Machine Learning (Decision Trees, SVMs, Logistic Regression, PCA, Kernel Methods, LDA, QDA)
• At least 1 year working with Deep Learning. Being fluent in at least one DL framework (e.g. Tensorflow, Theano, Keras, Mx Net, Py Torch, CNTK)
• Experience with data analytics
• Proficiency in SQL and Mongo DB
• Can self-manage a project from start to finish and be proactive to propose projects that can provide fruitful insights

業務内容

• Collaborate with the product and engineering teams to define and design the recommendation engine's architecture and algorithms

• Analyze user data to understand user behavior and preferences, enabling the development of personalized content recommendations

• Utilize statistical and machine learning techniques to analyze large datasets and derive actionable insights

• Conduct A/B testing to evaluate the effectiveness of recommendation algorithms and continuously refine the models

• Develop and implement machine learning models for content recommendation, personalized user experiences, and targeted content delivery

• Evaluate model performance and make data-driven adjustments to improve accuracy and relevance

• Prepare and preprocess raw data to ensure its suitability for analysis and modeling purposes

• Identify and address data quality issues to maintain the integrity of the recommendation engine

• Take a proactive approach in working closely with cross-functional teams, including (but not limited to) product managers, engineers, and designers, to align on data requirements and deliverables

• Communicate findings and insights effectively to technical and non-technical stakeholders

• Stay abreast of industry trends and advancements to propose innovative approaches and solutions

• Build state-of-the-art Deep Learning models to tackle Computer Vision and NLP problems

• Communicate with the stakeholders to understand the business problem, solve the problem using data, and effectively present the insights/results

その他待遇、手当等

• Competitive salary and performance-based bonus plus standard social security insurance

• Hybrid Work Environment

• Flextime: Core time 11:00 to 15:00

• For overseas candidates:

Visa Support, Re-location assistance to help you start your life in Tokyo, including first-time airfare to Tokyo, and hotel room for two weeks while we help you search for housing.

• Holidays: Annual leave, Summer as well as Winter Holidays, Childcare and Family care leave

• Support in buying the needed items to make your work easier: applications, large monitor even a good chair (unfortunately, pajamas not included; though, we are trying to get it approved soon)

• Transportation allowance OR if you live near the office, we will give you 20,000 JPY monthly towards housing

• We will subsidize books, language courses, conference tickets, and even paid apps.