In recent months, the impact of DeepSeek on the mutual fund industry has become increasingly evident, especially in light of the growing demand for talent among institutionsAs early as February, several public fund companies raced to kick-start their recruitment campaigns for the spring season, launching a new round of calls to potential applicantsParticularly notable is the notable increase in job openings related to artificial intelligence (AI) during this month, signaling a shift in the workforce needs of the industry amidst the ongoing localization surge driven by DeepSeek.

This sudden surge in hiring reflects a broader wave of transformation ignited by DeepSeek, as various public funds realize the potential benefits of this technological infrastructure for optimizing their operationsMany firms have already tasted the fruits of such initiatives, acknowledging the undeniable influence that advanced AI capabilities will have on the evolution of the fund sector.

As financial institutions widely adopt intelligent technologies, the journey towards a digital transformation that incorporates AI models will not be an overnight successLike the increasing emphasis many fund companies place on applicants’ abilities in AI technology development and its practical application, the seamless integration of DeepSeek into the fund business chain requires ongoing exploration and in-depth learning.

Positions focusing on algorithms and quantitative research have consequently garnered significant attentionFor example, on February 17, Huashan Fund announced three technology-related social recruitment positions, one of which was specifically for an AI algorithm engineerThe role outlines responsibilities encompassing a broad spectrum of cutting-edge AI tasks, such as researching and implementing machine learning and deep learning technologiesCandidates are expected to be proficient in popular algorithms and tools, particularly those with experience in large model development, such as ChatGPT and DeepSeek, along with substantial practical experience in training and optimization.

On a similar note, Huitianfu’s announcement on February 11 for a Senior IT Manager position explicitly highlighted AI application expertise as a critical requirement

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Candidates are expected to have mastery in Python programming and an in-depth understanding of mainstream machine learning frameworks, along with at least three years of experience in large language model trainingThis dual-angled role not only requires oversight of the entire model development lifecycle but also the design and implementation of solutions based on large language models to enhance core business efficiencies.

Meanwhile, E Fund followed suit on February 10, opening its doors for algorithm researchers, particularly targeting candidates with a doctoral degreeThis position encompasses responsibilities that include conducting modeling research tailored to real business scenarios and tracking advancements in AI academic research for application within financial contexts.

Smaller public fund companies also appear to be keen on welcoming talented individuals, aspiring to fill roles closely linked to machine learning, algorithmic analysis, and quantitative researchFor instance, Hongde Fund's new campus recruitment announcement on February 14 included an algorithm researcher position that emphasizes staying abreast of the latest research in deep learning and gleaning investment-relevant model insights to enhance high-frequency trading strategies.

Even funds participating in the early batch of recruitment for 2025 emphasized the role of quantitative researchers with a fundamental understanding of statistical and machine learning principles, tasked with developing stock selection models based on these methodologies.

Interestingly, not all public mutual funds are eager to bolster their talent reserves at this momentSome firms are taking a cautious approach, having already set up their frameworks for model applications, while others prefer a wait-and-see strategy before making substantial investments in recruitment, closely analyzing the practical benefits of their models.

A representative from a medium-sized public fund revealed that due to an early start in AI-focused research and a prior redistribution of manpower to support AI-related projects, their company does not currently pursue new recruitment actions

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They have already explored AI applications in depth and deployed privatized models even before integrating DeepSeek.

Another individual from a firm in the process of integrating DeepSeek conveyed that considering the industry’s current state, expansion is not yet on the horizonTheir company plans to follow emerging industry trends, evaluating the results and cost-benefit analyses of AI applications before determining if and when to recruit new talent.

The quest for new talent within the fund industry is closely intertwined with the transformative changes introduced by DeepSeek in the financial ecosystemFollowing the Spring Festival, many public fund institutions have publicly discussed their progress in deploying DeepSeekAs the middle of February approached, the momentum pushing for the adoption of new technologies continued unabated.

By February 16, both Caitong Fund and Western Capital Fund announced the successful local deployment of the DeepSeek model, joining several other firms that have recently disclosed their own integration effortsEven larger and prominent fund companies, while not disclosing detailed plans regarding DeepSeek, confirmed their internal projects to harness its potential.

Many of these funds are not new to the idea of leveraging AI for operational advantagesOver the past year, numerous mid to large-sized funds have integrated multiple prevalent AI models and even explored the development of proprietary modelsHowever, DeepSeek stands out due to its significantly lower training and inference costs compared to similar offerings, coupled with its enhanced usability and efficiency, providing an unprecedented impetus for the deployment of large AI models in the mutual fund realm.

Some firms have actively experienced the optimization benefits that DeepSeek has brought to their operationsAccording to the IT department head of Pengyang Fund, their marketing material compliance audit system has achieved an impressive rule hit rate of over 85%. However, the introduction of DeepSeek enhanced rule identification accuracy to over 95%, reflecting its superior analytical capabilities while exposing areas for further improvement in handling complex financial terms.

E Fund also indicated that the integration of DeepSeek has led to substantial upgrades in their in-house financial model, EFundGPT, enhancing both its analytical framework and deep thinking capabilities.

Fuguo Fund mentioned that through explorations by their tech team, the localized deployment of the model is now viable for various internal applications ranging from data processing, code generation, to enterprise-level RAG tasks.

Despite these advances, it is essential to recognize that the mutual fund sector's enthusiasm for DeepSeek does not equate to immediate and effortless integration into daily operations

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Currently, the application of DeepSeek has sped up across various areas, from marketing and customer service to compliance and operational management, significantly enhancing the efficiency and accuracy of institutional efforts.

However, the nuanced applications in investment research and analysis will take longer to mature due to development challenges and the adjustment period for staff learningAccording to one expert, applications of DeepSeek in investment research have inherent complexities that require time to navigate, citing tasks such as semantic analysis of research reports and the optimization of quantitative strategies as areas needing careful consideration.

Representatives from larger public funds similarly remarked that although their investment teams are encouraged to utilize AI as much as possible, most interactions with DeepSeek remain in experimental stagesA noted contrast emerges in the quant sector, where teams have started employing DeepSeek for training models, although the pace of progress remains uncertain.

While localizing DeepSeek’s deployment may appear straightforward, embedding AI solutions within business contexts presents a greater challengeAchieving differentiation in client-facing services alongside internal systems is paramountThis sentiment echoes through the recent recruitment efforts of several fund companies, which emphasize the dual necessity for applicants to maintain a balanced focus on both technological development and practical deployment capabilities.