As an Applied Data Scientist at Civis, I implemented the latest data science research to solve real-world problems. We recently worked with a global tool manufacturing company to reduce churn among their most loyal customers. A newly proposed tool, called SHAP (SHapley Additive exPlanation) values, allowed us to build a complex time-series XGBoost model capable of making highly accurate predictions for which customers were at risk, while still allowing for an individual-level interpretation of the factors that made each of these customers more or less likely to churn. To understand why this is important, we need to take a closer look at the concepts of model accuracy and interpretability. Until recently, we always had to choose between an accurate model that was hard to interpret, or a simple model that was easy to explain but sacrificed some accuracy.

Flexport is looking for a creative, technically-minded Applied Data Scientist who is motivated to solve some of the world's most challenging problems. Data is at the heart of our business and as an Applied Data Scientist, you will work to evaluate and provide insights into both our physical and digital products & features. In collaboration with multidisciplinary product and engineering teams, you will apply advanced methods, robust code, and transparent scientific rigor to creatively develop optimization products that will be embedded in software systems that deliver data-driven insights and automated decision making to end users where they need it, and when they need it. You will help to develop high performing individuals and serve as a technical leader and mentor to others. Our product and engineering teams collaborate very closely with our Applied Data Scientists to share domain knowledge, test hypotheses at scale and develop promising solutions that can be quickly and widely deployed.

In an earlier post, I shared the four foundations of trusted performance in AI: data quality, accuracy, robustness and stability, and speed. Today I want to talk about another important aspect of trusted AI: the software and human management that facilitate the use of AI. We think of this as the operational environment of an AI system. To create an ideal operating environment for AI, you need compliance, security, humility, governance, and business rules. Before you put a model into production, you may first need to clear compliance hurdles.

The B2B Insights & Analytics team exists to drive optimal decision-making that improves our business customers' FF&E shopping experience through Sales interactions. We seek to optimize Sales incrementality by identifying the most CLV-accretive Sales interactions with customers, as a critical driver of our flywheel. The data represents the voice of external customers (e.g. We source, interpret, and translate that voice into actions that serves our customers. Our team leads measurement - particularly around causal inference - and also plays a data challenger role to ensure the STO understands the "why" behind great customer experiences.

Launched in 2013, ManoMano is the European leader specialised in DIY, home improvement and gardening online. ManoMano brings together the largest offer of DIY & gardening online products. With more than 3600 seller partners and 10 million products, ManoMano brings together the largest offer across 6 countries: France, Belgium, Spain, Italy, Germany, United Kingdom. We currently are 650 Manas & Manos, including a quarter of international talents and 24 nationalities, working in our 4 offices (Paris x2, Bordeaux and Barcelona). People are at the heart of ManoMano's culture around our 3 core values: boldness, ingeniosity and care.

Enterprises today are transforming their businesses using Machine Learning (ML) to develop a lasting competitive advantage. From healthcare to transportation, supply chain to risk management, machine learning is becoming pervasive across industries, disrupting markets and reshaping business models. Organizations need the technology and tools required to build and deploy successful Machine Learning models and operate in an agile way. MLOps is the key to making machine learning projects successful at scale. It is the practice of collaboration between data science and IT teams designed to accelerate the entire machine lifecycle across model development, deployment, monitoring, and more.

METER Group, Inc. USA is seeking an Applied Data Scientist to join our product development team. This position will work in a cross-functional group of hardware and software engineers to develop systems that provide decision support and process automation for applications in the agricultural industry. The right candidate will have expertise in machine learning, model-data fusion, and computer science and demonstrate the potential to apply these skills to embedded sensing and IoT systems. This individual will be responsible for developing and applying algorithms that combine sensor data, models, and auxiliary information and output actionable insights and/or control of linked systems. We prefer candidates that are willing to relocate to our Pullman, WA office.