dovetail biopartners’ Post

Managing data teams effectively requires more than just hiring skilled scientists and bioinformaticians. It demands time, capital, and expertise to align their efforts with your goals. If you don’t have a data expert in a senior leadership position or the infrastructure to keep those teams focused and aligned, things can quickly spiral into inefficiency. We see this all the time at dovetail biopartners. Sometimes, we step in as the data team, building solutions from the ground up. Other times, we partner with existing teams—not to replace them, but to help them reach their full potential. How? 🔹 Turn chaos into clarity. Disorganized datasets or scaling infrastructure don’t have to be roadblocks. With tools like bioinformatics frameworks and cloud infrastructure, data can become accessible, actionable, and transformative. 🔹 Prioritize seamless integration. Solutions should align with existing workflows, enhancing pipelines and enabling the development of ML models without creating additional complexity. 🔹 Focus on partnerships, not handoffs. Effective solutions require ongoing support. Post-delivery collaboration ensures results are practical, scalable, and adaptable to evolving needs. 🔹 Deliver measurable results. Success comes from driving outcomes that matter—whether it’s accelerating go/no-go decisions or identifying new therapeutic targets. 🔹 Build for sustainability. Foundational data governance, predictive analytics, and scalable systems are the keys to long-term innovation and efficiency. The goal is to provide the frameworks, insights, and expertise to help teams move faster, work smarter, and deliver better outcomes. When research progresses efficiently, innovation becomes inevitable. Let us help get you there. Jump on a call with me here to learn more: https://lnkd.in/eaQCdaJC #datascience #ml #ai #translationalbiology #biotech

𝐓𝐡𝐞 𝐑𝐨𝐥𝐞 𝐨𝐟 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐜𝐞 𝐢𝐧 𝐇𝐞𝐚𝐥𝐭𝐡𝐓𝐞𝐜𝐡 𝐂𝐚𝐫𝐞𝐞𝐫𝐬 As the healthtech field progresses, the need for talented data scientists is growing which is why this post takes a close look at the significance of data science in healthtech careers. 𝐇𝐨𝐰 𝐢𝐭 𝐰𝐨𝐫𝐤𝐬 Data science uses algorithms, statistical models, and computer tools to analyze and make sense of data. One of the most important uses of data science in health technology is predictive analytics. Predictive models can tell future health trends and recognize patients who might be at risk of developing specific conditions. For instance, data science can forecast outbreaks of infectious diseases, which helps in taking quick action and allocating resources effectively. 𝐂𝐚𝐫𝐞𝐞𝐫 𝐎𝐩𝐩𝐨𝐫𝐭𝐮𝐧𝐢𝐭𝐢𝐞𝐬 𝐢𝐧 𝐇𝐞𝐚𝐥𝐭𝐡𝐓𝐞𝐜𝐡 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐜𝐞 Data scientists can pursue various career paths, including roles such as clinical data analysts, bioinformaticians, and health informatics specialists: 1. 𝐂𝐥𝐢𝐧𝐢𝐜𝐚𝐥 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐭𝐬 focus on analyzing clinical data to enhance healthcare delivery. Their work may involve optimizing clinical processes, reducing hospital readmission rates, and assessing the effectiveness of treatments. 2. 𝐁𝐢𝐨𝐢𝐧𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐜𝐢𝐚𝐧𝐬 specialize in analyzing biological data, such as genomic sequences. They play a crucial role in fields like personalized medicine, where treatments are customized based on an individual's genetic makeup. 3. 𝐇𝐞𝐚𝐥𝐭𝐡 𝐈𝐧𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐜𝐬 𝐒𝐩𝐞𝐜𝐢𝐚𝐥𝐢𝐬𝐭𝐬 concentrate on managing and utilizing health information systems. Their responsibilities include ensuring the accuracy and security of patient data, integrating different health IT systems, and supporting data-driven decision-making in clinical settings. In order to excel in the field of healthtech data science, it is important to have a combination of technical and industry-specific skills. Technical skills include: -𝑃𝑟𝑜𝑓𝑖𝑐𝑖𝑒𝑛𝑐𝑦 𝑖𝑛 𝑝𝑟𝑜𝑔𝑟𝑎𝑚𝑚𝑖𝑛𝑔 𝑙𝑎𝑛𝑔𝑢𝑎𝑔𝑒𝑠 𝑙𝑖𝑘𝑒 𝑃𝑦𝑡ℎ𝑜𝑛 𝑎𝑛𝑑 𝑅 -𝐹𝑎𝑚𝑖𝑙𝑖𝑎𝑟𝑖𝑡𝑦 𝑤𝑖𝑡ℎ 𝑚𝑎𝑐ℎ𝑖𝑛𝑒 𝑙𝑒𝑎𝑟𝑛𝑖𝑛𝑔 𝑎𝑙𝑔𝑜𝑟𝑖𝑡ℎ𝑚𝑠 -𝐸𝑥𝑝𝑒𝑟𝑡𝑖𝑠𝑒 𝑖𝑛 𝑑𝑎𝑡𝑎 𝑣𝑖𝑠𝑢𝑎𝑙𝑖𝑧𝑎𝑡𝑖𝑜𝑛 𝑡𝑜𝑜𝑙𝑠 𝑠𝑢𝑐ℎ 𝑎𝑠 𝑇𝑎𝑏𝑙𝑒𝑎𝑢 𝑎𝑛𝑑 𝑃𝑜𝑤𝑒𝑟 𝐵𝐼. 𝑈𝑛𝑑𝑒𝑟𝑠𝑡𝑎𝑛𝑑𝑖𝑛𝑔 𝑜𝑓 ℎ𝑒𝑎𝑙𝑡ℎ𝑐𝑎𝑟𝑒 𝑠𝑦𝑠𝑡𝑒𝑚𝑠 𝑎𝑛𝑑 𝑟𝑒𝑔𝑢𝑙𝑎𝑡𝑖𝑜𝑛𝑠 𝑡𝑜 𝑒𝑛𝑠𝑢𝑟𝑒 𝑑𝑎𝑡𝑎 𝑝𝑟𝑖𝑣𝑎𝑐𝑦 𝑎𝑛𝑑 𝑐𝑜𝑚𝑝𝑙𝑖𝑎𝑛𝑐𝑒. Soft skills include: 𝐶𝑟𝑖𝑡𝑖𝑐𝑎𝑙 𝑡ℎ𝑖𝑛𝑘𝑖𝑛g 𝑃𝑟𝑜𝑏𝑙𝑒𝑚-𝑠𝑜𝑙𝑣𝑖𝑛𝑔 and 𝐸𝑓𝑓𝑒𝑐𝑡𝑖𝑣𝑒 𝑐𝑜𝑚𝑚𝑢𝑛𝑖𝑐𝑎𝑡𝑖𝑜𝑛 The future of data science in healthtech is broad with advancements in AI and machine learning set to transform the field even further. We can expect to see innovations like AI-powered diagnostic tools, real-time analytics for patient monitoring, and personalized medicine, marking just the beginning of what's possible. #DataScience #DataScienceinHealthTech #Careeropportunitues

🚀 Data Scientists and Machine Learning Engineers in the Biotechnology Industry 🧬 The biotechnology industry is rapidly evolving, and with it comes an increasing demand for specialized talent. Data scientists and machine learning engineers are becoming indispensable as biotech companies harness vast amounts of biological and medical data to drive innovation, from drug discovery to personalized medicine. 🔬 Why the Demand? Advanced Analytics: Biotech firms rely on these professionals to analyze complex datasets—genomic sequences, protein structures, and more—to inform critical decisions. AI & ML Integration: These roles are key in implementing AI-driven solutions for disease diagnosis, patient management, and other groundbreaking applications. Growth Potential: With the rise of personalized medicine and electronic health records, the need for these experts is only set to grow. 🏥 Top Employers: Industry leaders like Illumina, Biogen, Amgen, and Gilead Sciences are at the forefront, actively seeking these talents to stay competitive. 💼 Navigating the Hiring Challenges: Finding the right candidates is no easy task. The specialized nature of the work and competition across industries make it difficult. That’s where Connexis Search Group comes in. We specialize in connecting biotech companies with top-tier data scientists and machine learning engineers, ensuring you have the talent needed to push the boundaries of innovation. 🔗 Looking to build your team with the best in the industry? Connect with Connexis Search Group today and let us help you find the perfect match for your organization’s needs. #Biotechnology #DataScience #MachineLearning #Recruitment #Innovation #ConnexisSearchGroup #BiotechTalent #AIinHealthcare https://hubs.ly/Q02LthQ20

I came across this amazing post by Dawn Choo(link in the comments)—first of all, such a fantastic explanation of the Case Study interviews example! 🙌 As I was reading through it, one particular concept caught my attention: Propensity Score Matching (PSM). Now, I’ll be honest—I didn’t quite understand what PSM was at first. But something about it made me curious. I couldn’t stop thinking, “How have I never heard of this before?” (especially as someone with a Master’s degree in Data Science 😅). But hey, every day’s a good day to learn something new, right? After all, nobody knows everything, and that’s the beauty of being in a field like ours—constant growth and learning. 🌱 So, I did what any data nerd would do—I went down the research rabbit hole. And let me tell you, PSM is one of those techniques that feels like a secret weapon once you truly understand it. It’s such a powerful way to solve problems where observational data needs to mimic randomized controlled trials. If you’re new to PSM too, I’ve put together a quick slide deck summarizing what I’ve learned about PSM: 1. What it is Propensity Score Matching (PSM) is a statistical technique used to estimate causal effects by creating a matched dataset based on propensity scores. This method is often employed to mimic randomized controlled trials in observational studies. It is a method to reduce selection bias and mimics randomized controlled trials to ensure better comparability. 2. When and why, it’s used PSM can be used to compare treatment effects in observational studies, balance covariates to improve fairness in comparisons and reducing biases in policy evaluation and healthcare analysis. 3. How it works Firstly, calculate propensity scores using logistic regression or machine learning to predict the probability of receiving a treatment based on observed covariates. Later, match similar pair individuals with similar propensity scores from treatment and control groups. Lastly, analyze the outcomes to estimate treatment effects. 4. A real-world example In healthcare, PSM is often used to compare the effectiveness of two treatments when random assignment is not possible. For example, comparing the outcomes of patients who underwent two different surgical procedures while accounting for baseline differences in age, severity, and comorbidities. Let me know what you think! Feedback is welcome—this is just my understanding from a few hours of reading. If you’re already a PSM pro, I’d love to hear how you’ve used it in your work. 🧠✨ Here’s to more “aha” moments and leveling up every day! 🚀 #PropensityScoreMatching #DataScience #StatisticalMethods #DataAnalysis #MachineLearning #DataDrivenDecisions #LearningEveryDay #ContinuousLearning #CaseStudyAnalysis #ProfessionalGrowth

Seeking Doctoral Research Study Participants Computer and Information IT specialists and senior experts Eligibility Criteria to Participate: ·      21 or older, ·      Employed at an organization with 250+ employees located in the USA, ·      currently working in leadership positions, such as IT administrators, coordinators, data analysts, engineers, managers, programming supervisors, directors, IT department managers, and other senior organizational managers with AI experience, ·      have three to five years of experience in IT/IS, with expertise in working with Artificial Intelligence applications, ·      and have a bachelor's degree or higher. This study is titled 21st-Century Technology in Telecommunications and Knowledge Workers Managing Unstructured Data: A Modified-Delphi Study. The purpose of this study is to measure consensus among AI experts regarding the most effective practices and future strategies for utilizing appropriate AI applications and techniques to assist telecommunication knowledge workers in managing unstructured data and information. All communications for this study will be completed digitally through secured email and online forms, Microsoft Outlook and Microsoft Forms. The participants will be asked to complete three online survey rounds for consensus. Round One’s completion time is estimated at 11 minutes. Round Two and Round Three’s completion times are yet to be determined due to the dependency on the analysis of Round One and Round Two’s results, but they are anticipated at 10 to 15 minutes each. To verify your eligibility and to participate in this research, click here or scan the code above. Please feel free to share this opportunity with other interested individuals. Upon verification and you are determined eligible, your email address will required to provide and send the Informed Consent Form to be signed and returned through an Abode Pro online form before participating in data collection, as well as notifications for any updates or changes in the study. All email addresses and data collected will be kept confidential. Please contact me privately if needed. The principal investigator for this study is Timeka Gillard, a doctoral student at the University of Phoenix, studying Management in Organizational Leadership and specializing in Information Systems and Technology. For any additional questions, comments, or concerns, please contact me privately through LinkedIn messaging, Msmeka88@email.phoenix.edu or 661-434-1211.

🔍 Unlocking the Power of Data: Transforming Industries Hey LinkedIn fam! 👋 Let's dive deeper into the world of data and its transformative impact on industries worldwide! 🌍 Data Revolution: Did you know that data is reshaping industries as we know them? From healthcare to finance and beyond, organizations are harnessing the power of data to drive innovation, improve efficiency, and enhance decision-making processes. 📈 Real-world Application: Take healthcare, for example. With the rise of electronic health records and wearable devices, healthcare providers can now analyze vast amounts of patient data to personalize treatments, predict diseases, and ultimately save lives. 🚀 Embracing the Future: As data professionals, we're at the forefront of this revolution. By honing our skills in data analysis, machine learning, and AI, we have the opportunity to shape the future of countless industries and make a meaningful impact on society. 💡 Takeaway for You: Whether you're already in the field or aspiring to join, remember that your expertise in data science or machine learning is invaluable. Keep learning, stay curious, and seize every opportunity to contribute to the data-driven future. 🚀 Let's Connect: Excited about the endless possibilities of data? Share your thoughts in the comments or reach out via message! Let's continue our journey towards unlocking the power of data together. 🌟 #DataScience #MachineLearning #AI #BigData #HealthcareInnovation #TechCareers #DataDriven #CareerGrowth #JobSeeker #TechJobs #Recruitment #Hiring #DataProfessional #AIRevolution #MLEngineer #FutureOfWork #Networking #JobSearch #CareerOpportunities

Data scientists: See the impact of your work! 🌍 Your contributions go beyond numbers; they change lives and make a difference! 🚑 In 𝗛𝗲𝗮𝗹𝘁𝗵𝗰𝗮𝗿𝗲 Data science is enabling: - Early Disease Detection: Predicts at-risk individuals. - Personalized Treatments: AI customizes patient care. - Healthcare Access: Connects remote areas with virtual assistants. 📚 In 𝗘𝗱𝘂𝗰𝗮𝘁𝗶𝗼𝗻 Transforming learning through: - Support for At-Risk Students: Flags those needing help. - Personalized Learning: Tailored content for students. - Resource Allocation: Data drives effective funding. 🌱 In 𝗘𝗻𝘃𝗶𝗿𝗼𝗻𝗺𝗲𝗻𝘁𝗮𝗹 𝗣𝗿𝗼𝘁𝗲𝗰𝘁𝗶𝗼𝗻 Making sustainable choices through: - Climate Prediction: Forecasting environmental impacts. - Resource Optimization: Reducing waste with AI. - Ecosystem Monitoring: Tracking changes for conservation. 🤝 In 𝗦𝗼𝗰𝗶𝗮𝗹 𝗦𝗲𝗿𝘃𝗶𝗰𝗲𝘀 Helping where it matters most: - Targeted Aid: Identifying areas in need. - Easier Benefits Access: Accelerating support delivery. - Continuous Program Evaluation: Improving aid programs over time. 💡 Data science can transform lives, but only if we prioritize: - 𝗣𝗿𝗶𝘃𝗮𝗰𝘆 - 𝗕𝗶𝗮𝘀 𝗣𝗿𝗲𝘃𝗲𝗻𝘁𝗶𝗼𝗻 - 𝗧𝗿𝗮𝗻𝘀𝗽𝗮𝗿𝗲𝗻𝗰𝘆 Where else could data science make an impact? Share your thoughts. Follow me to elevate your expertise in Data science and AI. #datascience #socialimpact #aiforgood #sustainability #healthtech #edtech

Are you honored? Yes, I’m Honored to be recognized as a 3x LinkedIn Top Voice in Data Analytics, Data Science, and Machine Learning! As a Data Scientist, & AI Specialist, I’m passionate about leveraging data to drive innovation and efficiency. I leverage my expertise in data and AI to solve complex problems and create a significant positive impact on my environment. By driving strategic data-driven decisions, I aim to enhance operational efficiency and unlock new opportunities for growth and development. 🏆 Top Machine Learning Voice 🏆 Top Data Analytics Voice 🏆 Top Data Science Voice My vision for the tech industry is to create an inclusive and innovative ecosystem where data and AI are harnessed to their full potential to benefit society. My goal is to continue pushing the boundaries of technology, making impactful contributions that drive sustainable progress and empower the next generation of tech leaders. Let's connect and discuss #DataCareer, #DataScience, #DataAnalytics, #DataEngineering, and #ProcessAutomation. 🔗 [Kudus Adewale Raji] #AI #DataScientist #TechInfluencer #ProcessAutomation #BPU #ForbesBLK #DataCareer #Innovation #Sustainability #TechVision

Did you know... - As of 2023, there are more than 500,000 unfilled IT positions in the US, with demand outpacing supply across all key IT roles. - About 79% of IT professionals are satisfied with their jobs, thanks to opportunities for growth, competitive pay, and working with the latest technologies. - Data science and analytics are some of the fastest-growing roles in IT, with demand rising over 50% each year due to the increasing focus on data-driven decisions. - The AI and Machine Learning fields are set to expand by over 30% in the next decade, as businesses continue to invest in automation, predictive analytics, and intelligent tools. #InformationTechnology #IThiring #ITjobs #AI #datascience #machinelearning

𝐄𝐱𝐜𝐢𝐭𝐢𝐧𝐠 𝐍𝐞𝐰𝐬 🎉: I recently graduated from The Ohio State University🎓 with my MSc in Computer Science and Engineering! I am #OpenToWork (available to start immediately ⏰) and actively seeking opportunities in data science and AI 🤖 . With my education and expertise, I aim to leverage AI and data science to drive business success 🚀. During my Master’s, I worked on a project guided by Rajiv Ramnath, where we developed an information retrieval system and automated recommendation system for pediatric healthcare using open-source LLMs (with RAG) and graph databases for Bloomalo. Post Graduation, I’ve been actively participating in industry conferences to stay up-to-date with the latest trends 📈. At AI Rising: • Srini K. shared that introducing AI in an organization is like adding a new employee , emphasizing the importance governance, guidance, and guardrails for responsible AI (Foundry for AI by Rackspace (FAIR™)).  • Nan Li's presentation outlined a roadmap🗺️ for building scalable AI by assessing business needs, organizational maturity, and technology feasibility; building the necessary people, processes, and technology; and measuring business impact, operational excellence, and industry gaps. • Tanya Berger-Wolf shared her vision and examples of trustworthy AI for biodiversity 🌿, exploring the opportunities and challenges in this domain.  • Erica Brooks shared her experience and expertise from the consulting industry 💼, discussing trends in AI and describing the potential impacts of leveraging Gen AI in current business setups. • J.D. Whitlock highlighted the importance of understanding the reality of Gen AI vs. Predictive AI through real-world examples that matter. At Techstars Columbus Powered by The Ohio State University, I gained firsthand experience where innovative startups like Sentius and Currents used cutting edge multi-model LLM's to solve complex business problems. At 𝐀𝐥𝐭𝐞𝐫𝐲𝐱 𝐯𝐢𝐫𝐭𝐮𝐚𝐥 𝐞𝐯𝐞𝐧𝐭: • 𝐇𝐞𝐚𝐭𝐡𝐞𝐫 𝐇𝐚𝐫𝐫𝐢𝐬 emphasized a business-driven approach to AI, engaging domain experts throughout data science workflow👩💼. • 𝐇𝐨𝐰𝐚𝐫𝐝 𝐃𝐫𝐞𝐬𝐧𝐞𝐫 noted that AI excels in marketing and corporations are using Gen AI to increase productivity 📊. • 𝐀𝐫𝐢 𝐊𝐚𝐩𝐥𝐚𝐧 discussed the challenges of adapting data stacks for AI and the transition towards data intelligence platforms 💻. While AI has been around for a long time, GenAI has created a new wave of change 🌊. Businesses across industries are bullish about increasing their adoption and view GenAI as a competitive opportunity rather than a threat[1][2]. I am eager to help organizations leverage AI and data science to achieve their business goals 🎯. If you know of any positions where my skills and passion can make a difference, let’s connect! 🤝 [1] https://shorturl.at/94bHy [2] https://shorturl.at/BNjBs #DataScience #MachineLearning #AI #DataAnalytics #DataMining #BigData #DataVisualization #PredictiveAnalytics