We are seeking a highly qualified and talented technologist to join the Data Platform team at Schonfeld. The team is re-envisioning Schonfeld's platform include the data pipeline, research infrastructure in the cloud and back testing. The platform will ideally allow PMs to analyze data, back test their strategies and deploy them to production trading seamlessly. We'd love if you had: The firm's ethos is embedded in our people. 'Talent is our strategy' is our mantra and drives how we approach all initiatives at the firm.

We are seeking a talented Data Scientist to join the Data Science team. The team is responsible for establishing best practices in the data pipeline as well as building large-scale data analytics and modeling for systematic strategies. The Data Scientist will collaborate closely with portfolio managers, data engineering, and operations teams to develop data cleaning and transformation processes, curate datasets, extract features, and generate signals using statistical and machine learning techniques for large-scale datasets. As a Data Scientist, you will acquire domain expertise for a wide range of financial datasets and conduct EDA to discover patterns, trends, and insights. Additionally, you will contribute to expanding a scalable data science environment that facilitates systematic data research through data and analytics sharing, modeling, dashboard visualization, and backtesting.

We are excited to offer an opportunity for a diverse group of talented students to join the internship program at Schonfeld. You'll spend ten weeks with the Data Operations team within the Schonfeld Technology division where you will be immersed in the culture and atmosphere of Schonfeld, working alongside talented professionals on high profile projects. The Data Operations team is responsible for observing and improving the production health of the Schonfeld Data Platform. The team provides specialist technical knowledge and data product expertise to Portfolio Managers, their investment teams, and other data users within Schonfeld. As part of the Data Operations team, you will be responsible for the availability and integrity of the data in the Schonfeld Data Platform.

CFA Institute, a global association of investment professionals, released the fourth report in its Future of Work series, exploring the Context, Culture, and Content of Work in the investment industry. Advancing technologies and hybrid working continue to change the shape of careers in finance. The Future of Work in Investment Management: The Future of Skills and Learning draws on CFA Institute survey data to identify the skills and learning equation for building talent and careers in a rapidly transforming investment industry. The report highlights current gaps between the supply and demand for skills in the investment industry, examines learning trends, and proposes changes to investment teams to better leverage diverse talent and the combined power of discrete but complementary skills. Fewer than half of survey respondents receive support from employers to develop the new skills they need.

Cathie Wood is CEO, CIO and founder of ARK Invest, a closely watched investment fund known for its prescient high risk, high reward strategy (Wood was early on Bitcoin and Tesla) and radical transparency (she explains her decisions to buy and sell in a public newsletter). ARK has had a wild ride in the last two years. Its flagship innovation ETF was up 150% in 2020, gaining Wood celebrity status, but has been down 38% in the past 12 months. Wood spoke with TIME about her long- and short-term predictions for the business world and what she has learned from her fund's bumpy ride. This interview has been condensed and edited for clarity. Typically, we lean into it, and this time is no exception.

We suggest a simple practical method to combine the human and artificial intelligence to both learn best investment practices of fund managers, and provide recommendations to improve them. Our approach is based on a combination of Inverse Reinforcement Learning (IRL) and RL. First, the IRL component learns the intent of fund managers as suggested by their trading history, and recovers their implied reward function. At the second step, this reward function is used by a direct RL algorithm to optimize asset allocation decisions. We show that our method is able to improve over the performance of individual fund managers.

It's been a long time since there was a hard line between a fundamental active manager and a quantitative investor using advanced computing techniques to uncover ideas. Even as the line has blurred recently, Michael Masdea, head of Wellington Management's Investment Science Group, said his number one job is to preserve the art in investing while bringing in sophisticated computing capabilities to support the firm's active managers. "We think that the balance of art and science is critical. Machines can't pick securities, but they can help a lot with the process around picking securities," said Masdea, who is a former semi-conductor analyst at Wellington as well as an equity portfolio manager. The Investment Science Group is focused on applying scientific techniques, such as data analytics, to everything from how portfolio managers come up with new ideas and implementing them to maximize returns, to developing professional investors, which includes uncovering and mitigating the downside of their behavioral biases.

AI is making strides at many levels in the world of investment management. Investors may already be riding the wave of artificial intelligence, unaware of the many ways they've been integrated. There are three main levels where AI is making a mark, says Amit Gupta, a managing director in Accenture's capital market industry group. At the first level, firms are using AI in back-office administrative tasks like net asset value calculations, reconciliation, settlement operations. At the second level, they use it in front-office tasks like client targeting and management, profiling of clients, personalization of service.

AI investment funds are not all about mega-caps like Alphabet, but about companies in other sectors using AI to increase competition, finds Fiona Rintoul. If you were to ask artificial intelligence (AI) how to save the planet, it would probably tell you to kill all humans, because they cause the pollution that creates climate change. But that would not be the response you wanted; therefore, you must recalibrate. "We need to ask the right questions of AI," says Rani Piputri, head of automated intelligence investing at NN Investment Partners. "In this situation, AI will nudge people to have fewer children."

We have witnessed a permanent shift in the role that data and technology are playing in investment decision-making. Idea generation techniques that had mainly been seen as emerging or experimental are now increasingly being adopted as mainstream. However, one of the biggest challenges for asset managers is how to incorporate, assimilate and integrate many of these techniques into the daily investment processes of the various investment teams. Regardless of the approach taken, data and how it is integrated and analyzed is going to play an increasingly pivotal role across all investment strategies. I will touch upon some key themes in this blog, but will go into more detail in a series to follow.