As a Real-World Data Scientist , you will play a critical role in harnessing real-world data to generate insights that support the development, approval, and commercialization of innovative drugs. You will work collaboratively with cross-functional teams to inform strategic decision-making and enhance patient outcomes.
Key Responsibilities:
? Data Acquisition, Analysis and Modeling:
o Collaborate with internal and external partners to acquire, validate, and integrate real-world data from diverse sources. Ensure data quality and integrity, and maintain comprehensive documentation for reproducibility
o Design and implement advanced statistical models and machine learning algorithms to extract insights from various real-world data sources, including electronic health records (EHRs), claims data, patient registries, and more.
o Analyze complex datasets to identify trends, patterns, and opportunities that can inform product development and market strategies.
? Support Real-World Studies/Health Outcomes Research:
o Design and execute real-world studies to generate real-world evidence, supporting the safety, efficacy, and economic value of products.
o Contribute to study design, including defining objectives, selecting appropriate methodologies, and developing analytical plans.
o Oversee and perform data curation and cleaning processes to ensure high-quality datasets for analysis.
o Collaborate with cross-functional teams to ensure the alignment of study objectives with business and regulatory goals.
? Insight Generation:
o Translate complex analytical results into actionable insights and communicate findings to stakeholders, including research scientists, clinical teams, and commercial partners.
o Develop data visualizations and reports that convey the impact of real-world data on clinical and business decisions.
? Innovation and Improvement:
o Stay updated with industry trends and innovations in data science, machine learning, and real-world evidence.
o Propose and develop new methodologies to improve data processing and analysis capabilities.
? Cross-Functional Collaboration:
o Work closely with clinical, regulatory, commercial, and technology teams to ensure alignment and integration of data-driven insights into business processes.
Qualifications:
? Education:
o Master’s or Ph.D. degree in Data Science, Biostatistics, Epidemiology, Health Outcomes, or a related field.
? Experience:
o Proven experience (3+ years) in data analysis, preferably in the pharmaceutical or healthcare industry.
o Experience working with real-world data sources, such as EHRs, claims data, or patient registries.
? Technical Skills:
o Proficiency in statistical and data analysis software such as SQL, R, SAS, Python, or similar.
o Experience with data visualization tools like Tableau, Power BI, or similar.
o Familiarity with machine learning frameworks and techniques.
? Soft Skills:
o Strong analytical and problem-solving skills.
o Excellent communication and presentation abilities to convey complex information to non-technical stakeholders.
o Ability to work effectively in a team-oriented, collaborative environment.
Preferred Qualifications:
? Experience in drug development processes or regulatory environments.
? Knowledge of healthcare systems, epidemiology, and outcomes research.
Other requirement
? May need travel and on-site data curation/analysis as required:
o Engage with local vendor to understand data sources and ensure proper data collection methods.
o Conduct on-site data curation and analysis to ensure immediate and accurate data handling, facilitating timely insights.
該崗位核心是利用現(xiàn)實(shí)世界數(shù)據(jù)（Real-World Data, RWD）生成洞見(jiàn)，為創(chuàng)新藥物的研發(fā)、審批及商業(yè)化提供支持，同時(shí)需與跨職能團(tuán)隊(duì)協(xié)作，助力戰(zhàn)略決策并改善患者預(yù)后。
一、核心職責(zé)
1. 數(shù)據(jù)獲取、分析與建模
? 與內(nèi)外部合作伙伴協(xié)作，獲取、驗(yàn)證并整合來(lái)自不同來(lái)源的現(xiàn)實(shí)世界數(shù)據(jù)，確保數(shù)據(jù)質(zhì)量與完整性，并維護(hù)詳盡文檔以保障可重復(fù)性。
? 設(shè)計(jì)并實(shí)施先進(jìn)的統(tǒng)計(jì)模型與機(jī)器學(xué)習(xí)算法，從電子健康記錄（EHRs）、索賠數(shù)據(jù)、患者登記系統(tǒng)等各類(lèi)現(xiàn)實(shí)世界數(shù)據(jù)源中提取洞見(jiàn)。
? 分析復(fù)雜數(shù)據(jù)集，識(shí)別可為產(chǎn)品研發(fā)和市場(chǎng)策略提供參考的趨勢(shì)、模式與機(jī)遇。
2. 支持現(xiàn)實(shí)世界研究/健康結(jié)局研究
? 設(shè)計(jì)并執(zhí)行現(xiàn)實(shí)世界研究以生成現(xiàn)實(shí)世界證據(jù)（Real-World Evidence, RWE），支撐產(chǎn)品的安全性、有效性與經(jīng)濟(jì)學(xué)價(jià)值。
? 參與研究設(shè)計(jì)，包括明確研究目標(biāo)、選擇合適的方法學(xué)及制定分析計(jì)劃。
? 監(jiān)督并執(zhí)行數(shù)據(jù)整理與清洗流程，確保用于分析的數(shù)據(jù)集質(zhì)量達(dá)標(biāo)。
? 與跨職能團(tuán)隊(duì)協(xié)作，確保研究目標(biāo)與業(yè)務(wù)及監(jiān)管目標(biāo)保持一致。
3. 洞見(jiàn)生成
? 將復(fù)雜的分析結(jié)果轉(zhuǎn)化為可落地的洞見(jiàn)，并向研究科學(xué)家、臨床團(tuán)隊(duì)、商業(yè)合作伙伴等利益相關(guān)者傳達(dá)研究發(fā)現(xiàn)。
? 制作數(shù)據(jù)可視化內(nèi)容與報(bào)告，闡明現(xiàn)實(shí)世界數(shù)據(jù)對(duì)臨床及業(yè)務(wù)決策的影響。
4. 創(chuàng)新與改進(jìn)
? 持續(xù)關(guān)注數(shù)據(jù)科學(xué)、機(jī)器學(xué)習(xí)及現(xiàn)實(shí)世界證據(jù)領(lǐng)域的行業(yè)趨勢(shì)與創(chuàng)新動(dòng)態(tài)。
? 提出并開(kāi)發(fā)新方法，以提升數(shù)據(jù)處理與分析能力。
5. 跨職能協(xié)作
? 與臨床、監(jiān)管、商業(yè)及技術(shù)團(tuán)隊(duì)緊密合作，確保數(shù)據(jù)驅(qū)動(dòng)的洞見(jiàn)能融入業(yè)務(wù)流程并保持方向一致。
二、任職要求
1. 教育背景
? 需持有數(shù)據(jù)科學(xué)、生物統(tǒng)計(jì)學(xué)、流行病學(xué)、健康結(jié)局或相關(guān)領(lǐng)域的碩士或博士學(xué)位。
2. 工作經(jīng)驗(yàn)
? 具備3年及以上數(shù)據(jù)分析經(jīng)驗(yàn)，有制藥或醫(yī)療健康行業(yè)經(jīng)驗(yàn)者優(yōu)先。
? 擁有處理電子健康記錄（EHRs）、索賠數(shù)據(jù)或患者登記系統(tǒng)等現(xiàn)實(shí)世界數(shù)據(jù)源的經(jīng)驗(yàn)。
3. 技術(shù)技能
? 熟練使用SQL、R、SAS、Python等統(tǒng)計(jì)與數(shù)據(jù)分析軟件。
? 掌握Tableau、Power BI等數(shù)據(jù)可視化工具的使用。
? 熟悉機(jī)器學(xué)習(xí)框架與相關(guān)技術(shù)。
4. 軟技能
? 具備出色的分析與問(wèn)題解決能力。
? 擁有優(yōu)秀的溝通與演示能力，能向非技術(shù)背景的利益相關(guān)者傳遞復(fù)雜信息。
? 能在團(tuán)隊(duì)導(dǎo)向的協(xié)作環(huán)境中高效工作。
三、優(yōu)先考慮條件
? 具備藥物研發(fā)流程或監(jiān)管環(huán)境相關(guān)經(jīng)驗(yàn)。
? 了解醫(yī)療健康系統(tǒng)、流行病學(xué)及結(jié)局研究知識(shí)。
四、其他要求
? 可能需根據(jù)工作需求出差并進(jìn)行現(xiàn)場(chǎng)數(shù)據(jù)整理/分析：
? 與當(dāng)?shù)毓?yīng)商對(duì)接，了解數(shù)據(jù)源并確保數(shù)據(jù)收集方法合規(guī)。
? 開(kāi)展現(xiàn)場(chǎng)數(shù)據(jù)整理與分析工作，保障數(shù)據(jù)處理的即時(shí)性與準(zhǔn)確性，助力及時(shí)生成洞見(jiàn)。