Here's how you can establish goals and objectives for performance evaluation as a data scientist.

Setting SMART goals relating to your key performance indicators is a great start. For example, you could target improving customer survival by two percent over a year. Measuring the revenue impact of this KPI in dollar terms and communicating it to the executive management team will transforms your team into a goal-driven data scientists. Achieving your set of objectives will help the firm to achieve its goals. Don’t forget that it’s quite easy to perform nice and interesting data science projects whilst failing to advance your firm’s performance so don’t get carried away by the fun. Your models must make a meaningful revenue impact.

Set clear, specific, and measurable goals that align with organizational objectives. Establish performance metrics that reflect the data scientist's contributions to achieving these goals, ensuring they are attainable and relevant.

Aligned Ambitions: Clearly define goals that align with your team and company objectives. Ensure your goals contribute to broader strategic initiatives.

One thing I've found helpful in defining goals is to start with a clear and specific vision of what you want to achieve. This specificity not only gives you a clear target but also makes it easier to measure progress. Quantifying your goals is essential—even if precise numbers are not always available. Using ranges and estimations can still effectively show the scale and impact of your efforts. This approach ensures that you are always moving towards a tangible objective, maintaining motivation and demonstrating progress. The key insight for making metrics achievable is understanding that goals don’t have to be 100% complete. Setting targets like 95% or 99% makes them more attainable in a shorter time frame.

As a data scientist, your impact can easily be measured by gauging the defined success metrics that were influenced by your analytics. Success metrics can consist of engagement, adoption, increment in mau/wau/dau or anything that drives organizational revenue. Any skills/tools you master, should directly be a function of impact/change in the defined success metrics. Foundational skills like statistics, data transformation, analytics and predictive modelling are the ones on the baseline, and you can top them up as needed.

Knowing your learning style is essential for effective skill mastery. Everyone has a unique way of absorbing and retaining information. Some people learn best through test-taking, others through hands-on experience, and some by talking to others. Understanding your preferred learning style can help you tailor your learning activities to be more efficient and enjoyable.

Evaluate the proficiency and development in key technical skills such as programming, statistical analysis, machine learning, and data visualization. Encourage continuous learning and professional growth to stay current with industry trends and advancements.

To evaluate performance effectively, focus on the impact of your projects. Define clear, measurable objectives that align with business goals, such as improving predictive accuracy, reducing processing time, or enhancing user engagement. Quantify the financial or operational benefits brought by your models, like cost savings or revenue growth. Track metrics such as model deployment rates, issue resolution times, and the scalability of solutions. Highlight case studies where your data science contributions led to significant organizational improvements. This focus on tangible, impactful outcomes ensures that your performance evaluations reflect the real-world value you add to the company.

Assess the tangible impact of completed projects on the organization. Measure success through key performance indicators (KPIs) such as cost savings, revenue generation, process improvements, and overall business value.

Gauge the ability to effectively communicate complex data insights to non-technical stakeholders. This includes clear reporting, compelling storytelling with data, and collaborative work with cross-functional teams.

You can influence people and show impact, if you can communicate well. The best thing you can do to learn this trait is - 1. Practice communicating your thoughts or talking on some topic with your friends or family. 2. Have a daily practice of speaking for 10 mins seeing the mirror (This can tell you what's going wrong!) 3. Try to incorporate difficult topics in your daily speaking sessions and ask for feedback. With time, you will see a huge change in your confidence and ability to communicate.

Any tech domain you see, is continuously evolving. If you are on this field, you have to pull your strings every day to stay relevant. I think perseverance and passion to drive things can bring in true innovation be it any tools or skills you acquire. You need to be a problem solver if you wish to innovate, irrespective of technology, tool or service that came into existence. Continuous practice and determination to expand your boundaries can bring in real change both in yourself and the world!

Encourage and measure innovative thinking and problem-solving. Evaluate contributions to new methodologies, tools, or approaches that enhance the data science practice within the organization.

Establish a Feedback Loop: Schedule regular check-ins with your manager to discuss progress towards goals, address challenges, and receive ongoing feedback to ensure you're on track.

Implement a robust feedback system for continuous performance improvement. Regularly gather and incorporate feedback from peers, managers, and stakeholders to identify strengths and areas for development.

Continuous Learning: Set goals around continuous learning. This could involve attending workshops, taking online courses, or participating in relevant conferences to stay current with the latest advancements in data science.